EX-Graph: A Pioneering Dataset Bridging Ethereum and X
Authors: Qian Wang
Zhen Zhang
Zemin Liu
Shengliang Lu
Bingqiao Luo
Bingsheng He
Venue: ICLR 2024 poster (2024)
Keywords: Ethereum
X
GNN
Primary area: datasets and benchmarks

Abstract: While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the breadth and depth of insight that can be derived. To address the above limitation, we introduce EX-Graph, a novel dataset that authentically links Ethereum and X, marking the first and largest dataset of its kind. EX-Graph combines Ethereum transaction records (2 million nodes and 30 million edges) and X following data (1 million nodes and 3 million edges), bonding 30,667 Ethereum addresses with verified X accounts sourced from OpenSea. Detailed statistical analysis on EX- Graph highlights the structural differences between X-matched and non-X-matched Ethereum addresses. Extensive experiments, including Ethereum link prediction, wash-trading Ethereum addresses detection, and X-Ethereum matching link pre- diction, emphasize the significant role of X data in enhancing Ethereum analysis. EX-Graph is available at https://exgraph.deno.dev/.

Published as a conference paper at ICLR 2024
EX-GRAPH: A PIONEERING DATASET BRIDGING ETHEREUM AND X
Qian Wang, Zhen Zhang; Zemin Liu, Shengliang Lu, Bingqiao Luo, Bingsheng He National University of Singapore (qi ansoc, zhen, zeminl iu, lusl @nus edu. .d. 1 luo. binggi wwngggioolee aoêu. wws. edu, hebs@comp. nus. edu.sg
ABSTRACT
While numerous public blockchain datasets available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the breadth and depth insight that can be derived. To address the above limitation, we introduce EX-Graph novel dataset that authentically links Ethereum and X, marking the first and largest dataset of its kind. EX-Graph combines Ethereum transaction records (2 million nodes and 30 million edges) and X following data (1 million nodes and million edges), bonding 30,667 Ethereum addresses with verified X accounts sourced from OpenSea. Detailed statistical analysis on EX- Graph highlights the structural differences between X-matched and non-X non-X-matcher Ethereum addresses. Extensive experiments, including Ethereum link prediction, wash-trading Ethereum addresses detection, and X-Ethereum matching link pre- diction, emphasize the sigmificant role of X data in enhancing Ethereum analysis. EX- EX-Graph is available at htthtts::/eee htttps://exgraph.ooo wwnggraph.dn.....
INTRODUCTION
Blockchain technology is known for its secure, unchangeable, and anonymous records (Gao et al. 2018). Among various blockchains, Ethereum is one of the largest and has shown substantial growth.
The market value of its cryptocurrency. ETH, hit 190 billion dollars as of September 23, 2023 Ethereum, attracting numerous celebrities to the blockchain (Nadini et al.) 2021). However, the anonymity inherent to blockchain has drawn various financial crimes such phishing scams and wash trading, particularly within
D Another example is Non-Fungible Tokens (NFTs), deployed
Ethereum, making research on Ethereum necessary (Dyson et al. 2019: Li et al. (020). Several studies released public Ethereum datasets to facilitate research in this area (Lin et al. 2022 Shamsi et al. 2022). In analyzing activities within Ethereum, researchers are increasingly employing graph analysis, specifically Graph Neural Network (GNN) methods (Liu al., 2022 Wang al. 2022).
However, prior studies have failed to address a notable issue: anonymous Ethereum addresses are hash strings which do not contain features outside of blockchain space. This absence of features limits the efficacy of GNN models. Two methods exist to get node features: manually designed features and network representation learning features (Li et al.) 2022). However, both types are derived from the Ethereum graph's structure. Recent research indicated that the interplay between features and the the graph structure is a key factor in the deterioration of GNN performance (Yang et al.) 2022). We note that some X users utilize the Ethereum Name Service (ENS) for their usernames, associating a auman-readable string with a specific Ethereum address. This connection indicates that the Ethereum address and the X account belong the same entity. One work (Béres et al.) 2021) enhanced Ethereum address features by linking 890 X users with ENS-format usernames.
Nevertheless, the reliability of such linkage is questionable as X without any verification. Drawing inspiration from a study (Vasan et al. 2022) which assembled an off-chain network of 14,706 NFT artists along with their verified Ethereum addresses and data,
can ENS-like names
"Corresponding author

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Published as a conference paper at ICLR 2024
Ethereum
Edge
Node
Match
Node
Etheraum
Address
Account
Edge
Edge
Transact
Follow
Figure 1: An overview of EX-Graph. The left figure displays the Ethereum transaction graph, showcasing transaction relationships between Ethereum addresses. The right X graph portrays the follow relationships among X accounts. A matching link identifies that the corresponding Ethereum address and X account belong to the same entity.
we consider integrating both off-chain and on-chain networks. This combination aims to enrich the analysis of on-chain activities by incorporating off-chain features. This approach also offers insights for enhancing graph learning real-world datasets which often lack sufficient features (Huo et al.) 2023): integrating features of the same entity from other domains via verified matching links.
To address the limitations of previous research, introduce dataset EX-Graph that bridges Ethereum transactions and X follower networks. Our choice of as an off-chain data source is due to its popularity among blockchain enthusiasts Gaasae-Brunng et al., (022). its accessible public API and the significant impact of tweets on Ethereum NFT market trends (Sawhney et al. 2022
Luo al. 2022 Tengland & Ask 2022). Additionally, we collect 30,667 links between Ethereum addresses and their corresponding X accounts from OpenSea E] ensuring the authentic ownership collect 1,445 labeled Ethereum addresses known participate in wash trading activities from Dune to facilitate Ethereum wash-trading addresses detection. As depicted in Figure [ EX-Graph consists of approximately million nodes, 33 million edges, and 30,667 matching links that bridge Ethereum addresses with corresponding X accounts.
of each account. Furthermore,
We further conduct comprehensive analysis of EX-Graph employing both statistical and empirical methods. Our statistical analysis reveals a notable difference in Ethereum activities between addresses, whether they are matched with X accounts or labeled for wash trading. We observe three key findings from empirical methods. First, integrating X boosts Ethereum link prediction by a peak of 8% AUC (Area Under the Curve) compared to the baseline without features. Second, the inclusion of significantly enhances the detection of wash-trading Ethereum addresses, elevating recall by up to 18% relative to the bascline excluding X features. Finally, integrating X supports the innovative task of predicting matching links between Ethereum addresses and their matching X accounts, achieving 74% AUC. Therefore, EX-Graph provides a valuable resource for future Ethereum- research. Our contributions can be summarized as follows:
We introduce EX-Graph, the most innovative and extensive open-source dataset bridging Ethereum transaction records and X follower network up to date. It lays the groundwork for deanonymizing on-chain activities by utilizing authentic relationships in off-chain contexts.
EX-Graph consists of the Ethereum Graph and the X Graph, unified by verified matching links. This inclusive dataset captures not only financial transactions the blockchain but also social interactions about Ethereum on X, providing a more holistic view of Ethereum.
Through detailed analyses and experiments on EX-Graph, we demonstrate that the integration of X data via verified matching links markedly enhances Ethereum task performances. EX-Graph not only facilitates blockchain research but also contributes to advancements in graph learning, uncovering opportunitics for integrating features of the same entity across various domains.
https::/devellgge X. https///eeeeleekkooooeeeeeeeeees
https://openses.
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2 RELATED WORK
In this section, we review NFT and social media, existing on-chain and on-chain/off-chain matching datasets. We only provide the most related studies here, and leave the rest to Appendix
NFT and social media. Non-Fungible Tokens, or NFTs, products the blockchain which captures much attention. They use blockchain to convert everything possible to digital files such as images, music, and videos (Kapoor et al. 2022). OpenSea is the largest NFT marketplace, which reached total market value of 31 billion dol lars volume (Luo et al. 2022). As social media become a place for NFT holders and investors attract public attention on NET projects (von Wachter et al.) 2022), some works researched on social media such as X, to analyze the relationship between the social media's contents and NFT market. Recent work examined the impact of Tweets on the NFT
market through Tweet text embedding (Sawhney et al. (022). keyword analysis (Luo et al. 2022).
frequency of Tweets (Tenlland Ask 2022), and username analysis (Kapoor et al. 2022). ) Results
of them showed that social media activity. especially on platforms like X, can indeed provide valuable
insights for predicting trends and behaviors the NFT market.
On-chain datasets. Many works proposed Ethereum datasets to promote research on Ethereum, focusing on Ethereum link prediction (Lin et al. 2020 2022 Wang et al., 2022), Ethereum account classification (Huang et al.
2022 Zhang et al., 2024), and Ethereum phishing accounts detection Chen et al. 2020; Li et al. 2022). In Ethereum transaction graphs, link prediction to predict whether there will be transactions between two Ethereum addresses. Phishing accounts detection is to detect which Ethercum addresses participate in phishing activities. Chartalist the first benchmark dataset for machine learning tasks the blockchain (Shamsi al. 2022). However, these datasets rely solely on Ethereum, which inherently contains few node features.
On-chain/off-chaie matching datasets. Few works developed datasets matching on-chain and off-chain activities from same entities. We find two datasets containing matching relationships. One work collected 890 ENS names from X handles profiles and mapped them to Ethereum addresses (Béres et al., 2021). This methodology. however, lacks reliability since X users can freely alter their displayed usernames. Another limitation of this dataset its exclusive focus on the Ethereum transaction graph, neglecting the X graph. The other work collected data from 13,487 NFT artists' X accounts, profiles, and Ethereum addresses (Vasan et al.) 2022). The limitation of this dataset its exclusive concentration on the off-chain social networks of artists, without inclusion of the on-chain transaction graph. We provide a summary of representative on-chain and on-chain/off-chain matching datasets as well our EX-Graph in Table I
Table Summary datasets featuring on-chain and off-chain matching. EX-Graph stands out due to its open-source nature, heterogeneity, and notably, it has the most extensive collection of on-chain and off-chain matching links compared to any other available datasets.
Open-
Helero-
Matching
#On-chain
On-chiain
Ffff-chain
#Off-chain
Wash-trading Addresses
Datasets
soureed
geneous
Links
Nodes
Edges
Nodes
Edges
Hunng et
1,401.200
2,815.028
D2 Lreesl 20p2
0
6,844.050
208,847.46
0
D3
20zz
0
40,635
1,095.245
0
D4 Shamms al
0
480,000
1,000.000
0
6.192
DS Bee
890
159,399
1,155,188
890
13487
14,066
EX EX-Graphi (ours)
30,387
2.610.465
29,583.858
1.103,509
3,768,281
1,445
PROPOSED DATASET
To enhance the analysis of on-chain data by incorporating off-chain information, we present EX- Graph (short for "Ethereum-X" Graph"), pioneering and large-scale open-souree dataset integrating Ethereum transaction data with X following data. This section details our data collection process and graph construction, visually presented in Figure]]
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(a) Data Collection
(b) Graph Construction
(c) Feature Extraction
On-chain Data
Ethereum Graph
Ethereum Transaction
Degree
Node/Type
Normal Address
Wash-trading
Wasi trading Address
Addresses
Structural Features
Matching Address
Matching Account
Matching X Accounts
Non matching Account
Off-chain Data
Edge/Type
BERT
Following
Semantic Festures
Follow
DeepWalk
Profilles
Structural Features
X Graph
Graph construction from dataset. This process initiates with the collection of on-chain, matching, and off-chain data, from which we construct on-chain and off-chain graphs. Subsequently, we extract features for Ethereum addresses and X accounts.
Figure 2:
3.1 DATA COLLECTION
In this section, we outline the process collecting on-chain, matching, and off-chain data. Through- out this data collection process, rigorously adhere to the privacy policies of OpenSea and X, ensuring utmost respect and protection for user privacy. For more details regarding our ethical considerations, please refer Appendix D
We acquire the required Ethereum transaction records by deploying a local Ethereum Virtual Machine (EVM) client, the runtime environment for executing smart contracts the Ethereum Hildenbrandt et al. 2018). This client facilitates the download of an exhaustive dataset encompassing Ethereum transaction records pertaining NFT from March 2022, August 31, 2022. This results in 2,6 10,465 unique Ethereum addresses and 29,585,858 transaction records between them, ensuring broad coverage of on-chain activities. Additionally, we enhance our on-chain data by gathering 1, 1,445 labeled wash-trading Ethereum addresses from Dune | enabling us to investigate and detect wash-trading addresses in Ethereum. More details are Appendix
On-chain Data:
We obtain X usernames linked to Ethereum addresses by utilizing the 'User' API endpoint from OpenSea. This data is sourced from OpenSea profile pages, resulting 30,387 entries that match Ethereum addresses with X accounts. More details are in Appendix F
Matching Data:
Off-chain Data: We utilize X's developer API's 'Followers' and 'Friends' endpoints to gather followers and following usernames associated with the selected X accounts. Specifically, 'Followers' are accounts who follow X account, whereas 'Friends' are accounts that a X account follows. Finally, collect 1,103,569 X accounts, interconnected by 3,768,281 follower-followin relationships. Furthermore, using the 'Users' endpoint of the X developer API, we collect the profiles of all matched X accounts. The total collection accounts 30,387 X profiles. These X profiles serve valuable off-chain data, enriching our analysis and complementing the Ethereum transactions. More details in Appendix G
3.2 GRAPH CONSTRUCTION
All collected data is presented in a heterogeneous graph G= (V, A, R). This graph captures the relationships between Ethereum addresses and X accounts, enabling a comprehensive analysis of their activities. The node set V comprises Ethereum addresses and X accounts, i.e., V Voth U Vx.
https:://unee http://uunee.com//ereeee////////
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Published as a conference paper at ICLR 2024
The edge set E consists of three types of edges: Ethereum transactions, Ethereum-X matches, and following relationships, ie., Eeth U Ematch U Ex. We define two functions T(v): : V + A and d(e) E + R. The function r(e) assigns node types, distinguishing Ethereum addresses as "Ethereum", and X accounts as "X" Similarly, the function Φ(e) assigns directed-edge types, categorizing Ethereum transactions as "Transact", Ethereum-X matches "Match", and X following relationships as "Follow". Importantly, each Ethereum transaction carries temporal information reflecting the timestamp of each transaction.
3.3 FEATURE EXTRACTION
We extract structural and semantic features for nodes. The structural features are sourced from the Ethereum graph, while the semantic features are drawn from the profiles of matched X accounts. Furthermore, we obtain embeddings for all X accounts using the Deep Walk algorithm in the X graph (Yuan et al. 2020). Drawing from the methodology articulated in (Chen et al. 2020), we select set of eight structural features that holistically represent nodes within the Ethereum transaction graph inspired by these insights. These features are degree, in-degree, out-degree, neighbors, in in-neighbors, out-net nut-neig the maximum number of transactions with neighbors, and average neighbor degree. An in-dephh description of these features in Appendix
We leverage BERT Devlin et al. 2018) for semantic feature extraction, converting the text profile s of each X account nt's 's text profile sa into 768-dimensional embedding h
h, BERT (ERT(ss), E 768
We choose BERT because of its proven effectiveness and widespread use in similar research. More details are in our Appendix C Then, enhance this semantic representation by appending the logarithm of the number of followers and followings, resulting in a 770-dimensional embedding h2 E ??70
Mr - h, (log(# followers 1), log(# followings 1)), h/ 770
This extended feature set then processed. Each node the Ethereum transaction and X following graph is equipped with an 8-dimensional structural feature set. Furthermore, every matched X account now possesses a 770-dimensional semantic feature set. To maintain uniformity across diverse feature sets and ensure a balanced model influence, implement Principal Component Analysis (PCA). This adjustment effectively reduces the dimensionality of the X semantic features to 8, denoted as hy E R
NM PC)(M,), A% TR..
STATISTICS AND EXPERIMENTS
In this section, first do an in-depth statistical analysis of our carefully curated EX-Graph dataset in Section[4.] The focus of this analysis the structural features mentioned in Section 3.3 of Ethereum addresses that are matched with X accounts versus those that are not, well differentiating wash-trading Ethereum addresses from non- non-wash-tres washhtrading ones (with the latter being treated as normal Ethereum addresses). Then conduct comprehensive experiments to address the following three research questions:
Q1: To what extent does X auxiliary information improve the performance of link prediction in Ethereum transactions?
Q2: How significantly does X auxiliary information enhance the detection of wash-trading addresses in Ethereum transactions?
03: Is it feasible to predict additional matching links based on known matching links?
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4.1
EX-GRAPH STATISTICAL ANALYSIS
4.1.1
OVERALL ANALYSIS
Table 2: Summary of EX-Graph. Wash-trading Addresses listed the row means how many wash-trading addresses match to X accounts.
Graph
Nodes
Edges
Avg. Degree
Matched Accounts
Wash-trading Addresses
Ethereum
2,610,465
29,585,858
11.33
30,667 30,667
1,445
1.103,509
3,768,281
EX-Graph combines the Ethereum and X graphs, as shown in Table 2 Ethereum has a higher average degree compared with X graph. However, the quantity of Ethereum addresses that match with a X account is notably small. The limited wash-trading addresses in the dataset highlight data imbalance. which often seen financial data. Only three wash-trading addresses match with X, reflecting the hidden nature of wash-trading activities. Overall, EX-Graph gives glimpse into Ethereum transactions and wash-trading behaviors, highlighting the difficulty in detecting wash-trading addresses in Ethereum.
4.1.2 ETHEREUM ADDRESSES BREAKDOWN
In terms of the eight structural features as mentioned in Section 3.3 in Table3 statistically compare the 30,667 X-matched Ethereum addresses with the of the Ethereum addresses. Similarly, for Ethereum graph, we further compare the 1,445 wash-trading Ethereum addresses with the other normal addresses, shown in Table
Table 3: Comparison between X-matched and non-X-matched Addresses.
X-matched Addresses
Non-X-matched Addresses
Features
Average
Median
Std
Average
Median
Std
Degree
28.26
2.00
492.39
12.98
2,00
80.75
In-degree
15.24
1.00
309.76
6.48
1.00
38.55
Out-degree
13.03
1.00
185.41
6.50
1.00
51.65
Neighbors
26.58
2.00
402.22
12.64
2.00
73.85
In-neighbors
15.24
309.76
6.48
1.00
38.55
Out-neighbors
13.03
1.00
185.41
6.50
1.00
51.65
Max. ixns with neighbors
6.78
3.00
27.94
2.89
1.00
30.42
Average neighbor degree
33076.00
31105.75
27921.99
1442.87
149.00
7170.93
Table 4: Comparison between wash-trading and normal addresses on Ethereum graph.
Wash-Trading Addresses
Normal Addresses
Features
Average
Median
Std
Average
Median
Std
Degree
137.61
15,00
377.70
13.02
2.00
92.74
In-degree
50.53
7.00
115.58
6.52
1.00
48.46
Out-degree
87.09
6.00
283.29
6.50
1.00
53.83
Neighbors
134.66
14.00
371.35
12.67
2.00
82.41
In-neighbors
87.09
6.00
283.29
6.50
1.00
53.83
Out-neighbors
50.53
7.00
115.58
6.52
1.00
48.46
Max. txns with neighbors
18.38
8.00
55.60
2.91
1.00
30.37
Average neighbor degree
423.95
247.84
986.01
1740.28
151.71
8220.95
From Tables and 4 clear that X-matched and wash-trading Ethereum addresses show higher activity levels across the first seven features compared to others. The increased in-degree, out- degree, and maximum transactions with neighbors suggest X-matched addresses are part of an active sub-network within Ethereum. Wash-trading addresses' higher degree, and number of neighbors underscore their active transaction behavior. These addresses exhibit higher median in-degree, out-degree values, and larger average degree, indicating significant transaction volumes.

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Published as a conference paper at ICLR 2024
The significant average neighbor degree for X-matched addresses primarily reflects frequent trans- actions with high-degree nodes, such as smart contracts in activities like NFT minting (Wang et al.) 2021). Contrastingly, wash-trading addresses exhibit lower average neighbor degree, likely due to their circular transactions among a limited group of addresses. This behavior restricts interaction
with the high-degree nodes of the wider Ethereum network (Victor & Weintraud 202T
Luo et al..
2023). We provide a more detailed analysis in Appendix H
In conclusion, we highlight notable differences in transaction activities and network centrality between X-matched and non-X-matched Ethereum addresses, as well as between wash-trading and normal Ethereum addresses. In the following experiments, we explore the correlation between these structural features and Ethereum transaction link prediction, with special focus on the classification of Ethereum addresses, especially wash-trading addresses.
4.2 ETHEREUM LINK PREDICTION (Q1)
Ethereum link prediction can help monitor transaction trends of Ethereum. However, previous works
only research on Ethereum dataset (Lin al. 2021 (Lin al. 2022). Therefore, we aim compare
the performance between scenarios: one with the addition of X features and one without. We employ
eleven baseline models: Deep Walk (Perozzi al. 2014). Node2Vec (Grover & Leskoved 2016).
GGNN (Li et al. (2015), GCN (Kipf & Welling 2016), GAT (Veličković et al. GraphSage
(Hamilton et al. 2017). APPNP (Gasteiger et al. 2018). TAGCN (Du et al. 2017). Cluster-GCN
(Chiang et al. 2019). DAGNN et al. 2020). and GATv2 (Brody et al. (021)
Implementation details. Firstly. We divide all Ethereum graph edges into training, validation, and test sets, following 70/10/20 ratio based on edge timestamps. We then remove the validation and test edges and choose the largest connected subgraph from the remaining edges to constitute our training graph. This process ensures that we only use old links to predict new ones. Next, we focus on all edges within our training graph that are connected to at least one X- X-matched Ethereum address to serve as positive training edges. For balance, we generate equal number of negative training edges by selecting a non-existent link from each X-matched Ethereum address within the positive training edges. For the validation and test sets, choose positive edges from the previously partitioned validation and test sets and ensure that both nodes in a validation or test edge are also present in the training graph.
For every positive edge within the validation and test sets, we randomly choose a non-existent link stemming from the same X-associated Ethereum address. We provide details of this setup in Appendix [ With this framework, we execute two experiments: one that incorporates X features and one that does not. Our goal of X data on the analysis of Ethereum activities.
compare the results of these two scenarios to determine the influence
Evaluation metrics. We use the following four widely used metrics (Chen et al. 2020). to evaluate the performance of experiments in a comprehensive way: (1) AUC-ROC, (2) Precision, (3) Recall, and (4) Fi-score. Note that, for the experiments we repeat 5 times and report the average performance with standard deviation.
Table 5: Results for Ethereum link prediction, with f. and indicating performance increase, maintaining and decrease, respectively.
Method
Without X Features
With X Features
AUC-ROC
Precision
Recall
Fl
AUC-ROC
Precision
Recall
Fl
DeepWalk Node2Vee
0.72.10.00
0.80
0.57.10.00
0.60
0.74....00 (t)
0.00 (1)
0.60 (f)
0.70..0.00 (t)
0.72:0.00
0.8110.00
0,57:20.0
0.67:0000
0,74100.0
0.8300000
0.60=0.00 (?)
0.7010.00 (t)
GCN
0.76:2001
0.7020.01
0.6720002
0.69:2001
0.81:00000 (t)
0.8100011 (1)
0.77=0.02 (1)
0.75:0011 (f)
GAT
0.77=0.01
0.73+0.02
0.67=0.02
0.70=0001
0.81
0.75=0001
0.76=0.01 (
0.75+0.01
GATv2
0.77:0001
0.72:10.01
0.7420.02
0.73=0.01
0.7920.00 (t)
0.73=0.01 (1)
0.75=0.01 (1)
0.7410.00 (f)
GraphSage
0.84.10.0
0.77.10.01
0.77.10.01
0.7810.00
0.86.10.00 (t)
0.81_0.01 (1)
0.7910.01 (T)
0.80.00.00 (f)
TAGCN
0.8120001
0.82:10.01
0.6420.02
0.72=0.01
0.88 (t)
0.85-0011 (1)
0.73=0.01 (1)
0.7810.00 (f)
Cluster GCN
0.82.10.0
0.78.10.01
0.70.10.01
0.74.10.01
0.3610..00
0.8100.0 (1)
0.7510.01 (?)
0.78
DAGNN
0.79:20.00
0.77::0011
0.67-20.1
0.72=0.00
0.87
(f)
0.820000 (1)
0.77=0.01 (1)
0.7910.00 (f)
APPNP
0.82.10.01
0,80.10.33
0.68 1..02
0.74.10.1
0.8910.05 (1)
0.80 (1)
0.78 (?)
0.81
GGNN
0.82:2001
0.8111001
0.68:20.11
0.74.0.00
0.84 (1)
0.79-0001 (1)
0.73=0.02 (1)
0.7610.01 (T)
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Discussion. Results shown in Table 5 demonstrate the superiority of deep learning models that leverage both graph structures and X features compared to those that rely solely on graph structures. Notably, incorporating features from matched X accounts significantly improves the performance of all models, as evidenced by enhanced metrics. For instance, APPNP exhibits a substantial 7% increase in AUC-ROC when integrating X features, while DAGNN shows an 8% increase in AUC-ROC and remarkable 10% surge in recall. This improvement is attributed to the fact that by incorporating X data, we gain valuable insights into the behavior and characteristics of these addresses that are not available from Ethereum transaction data alone.
4.3 WASH-TRADING ADDRESSES DETECTION (Q2)
Wash trading is a common fraudulent practice on Ethereum. Its usual objectives are manipulating a
cryptocurrency'' 'ss price and facilitating money laundering (Cao al. 2015). Detecting wash-trading
addresses can help protect the honest users on Ethereum. To assess the effectiveness of including X information in detecting these addresses, we employ the same eleven baseline models in Section 1.4
First, we split the whole Ethereum graph into training, validation, and test- ing graphs following a 70/10/20 ratio based on edge timestamps. This ensures temporal consistency and upholds the transaction sequence, as node features stem from their edges and neighbors. Then we keep the largest connected subgraph of the training graph for training baseline models.
Implementation details.
Given the significant imbalance between the wash-trading Ethereum addresses and the normal Ethereum addresses approximately 0.05% and 99.95% respectively we adopt a balanced sampling strategy. Specifically, all wash-trading addresses that appear in the training, validation, and testing graphs are considered as positive samples. We randomly choose an identical number of normal addresses from each of the train/valice crann/valdaaiion/esst graphs negative samples. This strategy ensures balanced positive and negative sample distribution. Under this setup, we conduct two experiments for each model- one that incorporates X features and one that does not. The objective is compare the performance between these two situations to see how much X can affect the classification of wash-trading Ethereum addresses. More implementation details are in Appendix [
Evaluation metrics. We employ the following four widely used metrics: (1) AUC-ROC, (2) Precision, (3) Recall, and (4) Fl-score. Among these metrics, we place higher emphasis on recall, as it depicts the percentage of detected wash-trading addresses out of all wash- musshttaading addresses (Chen et al. 2020). Prioritizing recall underscores a preference for navigating false risk warnings over experiencing deception. For each experiment, we repeat it 5 times and report the average performance along with the standard deviation.
Table 6: Results for wash-trading addresses detection.
Without X Features
With X Features
Method
AUC-ROC
Precision
Recall
F1
AUC-ROC
Precision
Recall
Fi
Deep Walk
0.54:0000
0.54:0.00
0.5920.00
0.56:0000
0.55200.0
0.55_0.00
0,5820.00 (L) 0,57:0.00 (t)
Node2Voc
0.54
0.54.10.00
0,5910.00
0.56
0,55 (1)
0.55_ (1)
0.58 ())
0.5710.00 (f)
GCN
0.7910.01
0.8110.02
0.56:10.01
0.66:10.1
0.79:10.01 (-)
082-0.01 (1)
0.5610001 (-)
0.6611001 (-)
GAT
0.80=0.01
0.80+0001
0.59=0000
0.68=0.00
0.80= (f)
0.74=0.01 (})
0.72=0.00 (f)
0.73=0000 (f)
GATv2
0.80:00000
0.76.10.01
0.68:10.01
0,72:10.00
0.81110011 (t)
0.74_0.02 (1)
0.74:0.02 (?)
0.741001 (t)
GraphSage
0,78=0001
0.81+0001
0.52=0001
0.63=0.01
0.80=20.01
(1) 0.75=0.01 ())
0.70=0.02
(?)
0.72=0.01 (?)
TAGCN
0.80::001
0.76:10.01
0.67:10.01
0.71:10.1
0.80:10.01
(-) 0.74=0.01 (
0.73:0001 (?)
0.7310001 (t)
Cluster-GCN
0.80=0001
0.7420.01
0.69=0001
0.71=0.01
0.81 (f) 0.75=0.02 (1)
0.72=0.01
(?)
0.7420000 (f)
DAGNN
0.80:00.00
0.74:10.01
0.71:10.00
0.72:10.0
0.8110000
(t) 0.75_0.01 (1)
0.73:0022 (1)
0.7410001 (f)
APPNP
0.80=0001
0.74+0.01
0,70=0001
0.72=0.00
0.81 (1)
0.75=0.01 (1)
0.73=0.01
(?)
0.74+:0.0 (f)
GGNN
0.79:0001
0.75:10.0
0.69:10.2
0.72:10.01
0.80:1.011 (1)
0.75 (1)
0.72:0.01 (f)
0.7310.01 (t)
Discussion. reveals that models like DeepWalk and Node2Vec underperform as they do not utilize node features. The relatively low recall in GNN models illustrates the challenge in detecting wash-trading addresses from the heavily imbalanced data as normal addresses significantly outnumber fraudulent ones. The integration of X and structural features notably enhances AUC-ROC, recall, and Fl scores across most models, particularly boosting the recall score by 18% in GraphSage and 13% in GAT. Despite observed trade-offs between precision and recall in some results, method marks substantial progress in detecting these fraudulent activities, showcasing the benefits of integrating both Ethereum and X data in identifying wash-trading activities in Ethereum.
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4.4 MATCHING LINK PREDICTION (Q3)
In this section, we aim to predict matching links between X accounts and Ethereum addresses to further enrich our dataset. We utilize thirteen baseline models for this task. Among these, Deep Walk (Perozzi et al. 2014), Node2 (Grover & Leskovec, 2016), and HIN2Vec (Fu et al.) (017) operate
without node features, whereas the others incorporate them. Specifically, GGNN (Li al. 2015).
GCN (Kipf & Welling, 2016), GAT (Veličković et al. (017)) GraphSage (Hamilton et al. 2017)
APPNP (Gasteiger et al. 2018). TAGCN (Du et al., 2017). Cluster-GCN (Chiang et al. 2019);
DAGNN (Liu et al. 2020), and GATv2 (Brody et al.
2021 are designed for homogeneous graph
embeddings. In contrast, HeN (Fu et al., 2017) and R-GCN (Schlichtkrull et al., 2018 are
tailored for heterogeneous graph embeddings.
Implementation details. In this study, we focus on the largest connected subgraph extracted from the whole EX-Graph we have created. As our matching edges do not incorporate temporal data, randomly partition all matching edges into training, validation, and testing sets in a 70/10/20 split respectively. We treat all matching edges as positive samples. For the negative samples, we opt for random selection approach where we pick non-existing connections between Ethereum addresses and all their matched X accounts. This method ensures maintain balanced 1-to-1 ratio between positive and negative edges. Further implementation details can be found in Appendix K
Evaluation metries. We the following four widely-used metrics evaluate the performance of experiments in comprehensive way: (1) AUC-ROC, (2) Precision, (3) Fi-score, and (4) Accuracy. For each experiment, we repeat times and report the average performance with standard deviation.
Table 7: Results for matching link prediction (the best results are bolded).
Method
AUC-ROC
Precision
DeepWalk Vece2Vec
0.42=0.00
0,3110.01
0.1810001
0.42:10.00 0.42:10.00
0.42::0.0
0.32:10.01
0.18:00.1
GCN
0.71=0.01
0.64+0.01
0.65+0003
0.71+00.01
GAT
0.60=0.00
0.5210.03
0.61100.1
0.56110.01
GATv2
0.48=0001
0.50+0.02
0.44++0.00
0.50++0.01
GraphSage
0.67=0.02
0.63+0.02
0.71+0.02
0.67+0001
TAGCN
0.66-10.03
0.6310.02
0.671:0.01
0.6510001
Cluster-GCN
0.67-10.11
0.6510.01
0.66110.01
0.65110.01
DAGNN
0.74-0.00
0.66+0000
0.79+0.00
0.74+0.00
APPNP
0.73=0.01
0.69:0.000
0.72+00.0
0.70=00.0
GGNN
0.71-0001
0.6510.000
0.76110.01
0.7110.01
R-GCN
0.66+0.00
0.66+0.01
0.65+00.00
0.66+00.00
HIN2Vec
0.49=0.00
0.49:0.01
0.36:10.01
0.49:00.00
Discussion. As illustrated in Table 7 DeepWalk and Vec perform poorly due to their limited model complexity and grasp of node features during training. HIN2Vec, which also doesn't 't leverage node features, performs suboptimally compared to the other GNN models. DAGNN excels due to its ability to capture large-scale graph information effectively (Liu et al. 2020). Other GNN models yield comparable results across all metrics, suggesting both homogeneous and heterogeneous models are similarly effective for this task. Furthermore, the similar results highlight the considerable potential of GNNs to accurately forecast additional matching links, enhancing the richness of our EX-Graph.
5 CONCLUSION
We present EX-Graph, a pioneering and extensive dataset that bridges Ethereum and X. Experiments on EX-Graph highlight integrating additional X data significantly boosts the effectiveness of various tasks on Ethereum. Our research facilitates the understanding of the interplay between on-chain and off-chain worlds and paves the way for novel methods in probing on-chain activities using off-chain supplementary data. However, we recognize that there are still areas to work on, such collecting more matching links between on-chain and off-chain graphs, and wash-trading Ethereum addresses to counter the data imbalance. Our next steps involve incorporating these enhancements and constantly updating our graph with more on-chain and off--hain nodes and edges.

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ETHICS STATEMENT
Our research adheres rigorously to ethical standards in data collection from both X and OpenSea. We obtained X interaction data in compliance with its guidelines, and associated X accounts with Ethereum addresses using exclusively public information. To ensure privacy, all handles were anonymized, and our data sharing abided by X's academic redistribution policies. Through these meticulous measures, we achieved a responsible balance between insights and privacy across blockchain and social media domains. Our research, focused on public and pre-anonymized data, is under exemption review by the Institutional Review Board (IRB), supervised by the faculty. We remain committed to upholding ethical and privacy guidelines, ensuring our methodologies respect the protections inherent to human subjects.
7 REPRODUCIBILITY STATEMENT
We have publicly released our dataset and codebase at ltttps://github EX Graph, which includes the datasets, models, and settings necessary to reproduce our results.
ACKNOWLEDDEMMEN
This research supported by the National Research Foundation, Singapore under Al Singapore Programme (AISG Award No: AISG2-TC-2021-002)
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APPENDIX
A EXPERIMENTAL ENVIRONMENT
All models in our experiments were implemented using Pytorch 2.0.0 in Python 3.9.16, and run on a robust Linux workstation. This system is equipped with two Intel(R) Xeon(R) Gold 6226R CPUs, each operating at base frequency of 2.90 GHz and a max turbo frequency of 3.90 GHz. With 16 cores each, capable of supporting 32 threads, these CPUs offer a total of 64 logical CPUs for efficient multitasking and parallel computing. The workstation is further complemented by a potent GPU setup, comprising cight NVIDIA GeForce RTX 3090 GPUs, each providing 24.576 GB of memory. The operation of these GPUs is managed by the NVIDIA-SMI 525.60.15 13 driver and CUDA 12.0, ensuring optimal computational performance for our tasks.
To implement and train our graph- based models, we utilized the version 1.1.0 of the Deep Graph Library (DGL)) which is popular open-source library designed to simplify the development of deep learning models for graph- structrrdd data. By leveraging DGL's optimized performance and flexible APIs, we were able to casily construct, manipulate, and train graph neural networks (GNNs) on large-scale graphs.
B SUPPLEMENTAL RELATED WORK
Graph representation learning. Graphs are powerful tools for representing complex data and relationships, they can effectively encapsulate vast amounts of information (Cao et al. 2016). Among various methods for graph analytics, graph representation learning stands out for its ability to convert -igh-dimensional graph data into dimensiinnii space while retaining essential information (Cai et al.] 2018). Two major categories of graph representation learning techniques emerge, including those based on random walks and deep learning (Goyal & Ferrara 2018). Random walk methods such as DeepWalk (Perozzi et al. 2014) and Node2Vec (Grover & Leskovec) 2016) maintain higher-order proximity between nodes. They achieve this by maximizing the likelihood of consecutive nodes appearing in random walks of fixed lengths, thereby approximating properties in large graphs (Goyal & Ferraral 22018). On the other hand, deep learning- based graph represen- tations, such as Graph Convolutional Network (GCN) (Kipf & Welling, 2016). Graph Attention Network (GAT) (Veličković et al. 2017), and GraphSAGE: (Hamilton et al.) (017). utilize iterative convolution operators to aggregate neighbor embeddings. This approach enables scalable learning of node representations, capturing the global characteristics of the neighborhood (Goyal & Ferrara 2018). For homogeneous graphs, popular embedding methods include DeepWalk (Perozzi et al.
2014).
Node2 (Grover & Leskoved 2016). GCN (Kipf & Welling 2016), GAT (Veličkovid 2017), and GraphSage (Hamilton al. 2017). For heterogeneous graphs, methods such as Vec (Fu et al. (017). Relational Graph Convolutional Network (R-GCN) (Schlichtkrull et al.
al.
HIN2
2018
Heterogeneous Graph Attention Network (HAN) (Wang al. 2019), Heterogeneous Graph
Transformer (HGT) (Hu et al. 2020), and Metapath Aggregated Graph Neural Network (MAGNN)
Fuu et al. 2020) have been proposed and utilized. These embedding methods have been widely used in numerous downstream tasks, such as link prediction (Al Hasan et al. (2066) and node classification (Rong al. (2019). Furthermore, to meet the privacy protection requirements in graph- data such as X, many graph representation learning approaches utilize federated learning methods (Tang
ct al. 2022a b).
C
FEATURE EXTRACTION
In this section, we elaborate the feature extraction process, detailing the nature and importance of each feature extracted for our study.
C.I STRUCTURAL FEATURE EXTRACTION
Structural features consist of two parts: DeepWalk embeddings, and eight statistical features.
https://gi thtb:://gggttbtuueoo wwm/ c/dg1
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Weep In study, deploy the DeepWalk model to generate embeddings that capture the structural features of the network. Each X account is represented within a 128-dimensional embedding space, striking balance between detail and computational efficiency. The model navigates through the network with a walk length of 40 and a context size of 5, allowing for an extensive exploration of the graph and focus local node interactions. We have set the number of walks per node to 10, ensuring diverse and robust random walks for each node. Additionally, for an effective learning process, the model employs a strategy of using a single negative sample against each positive instance. This configuration of the DeepWalk model is integral to our approach in accurately representing the complex relationships within the X network.
Degree. This is a measure of the node's 's overall connectivity, representing the total number of transactions either sent or received by the node. It provides insights into the node's activity level within the Ethereum network.
In-degree. This feature reflects the number of incoming transactions to the node from other nodes. high in-degree indicates node of significant interest or utility within the network.
Out-degree, This counts the number of transactions that the node initiates, i.e., sends out to other nodes. A high out-degree could suggest an active node that frequently interacts with other nodes.
Neighbors. This feature records the total number of unique nodes with which the node has connection, be it via incoming or outgoing transactions. It provides a sense of the node's direct reach within the network.
In-neighbors.
This is the count of unique nodes that have transactions with the current node, illustrating the diversity of nodes sending transactions to the node.
Out-neighbors. This is the number of unique nodes to which the current node sends transactions, illustrating the diversity of nodes with which the node interacts.
Maximum number of transactions with neighbors. This feature reflects the highest transaction fre- quency the node has with any of its neighbors, providing insight into the node's strongest connection in terms of transaction count.
Average neighbor degree. This refers to the mean degree of all the node's neighbors. gives a sense of the general connectivity and activity level of the nodes directly linked to the node in the graph.
C.2 X SEMANTIC FEATURE EXTRACTION
We leverage BERT (Devlin et al. 2018) for semantic feature extraction, transforming each X profile into a 768 768-dimensional embedding. We choose BERT for the following two reasons.
Proven efficacy in text representation: BERT has been widely recognized for its ability to generate semantically rich representations of textual data. This is primarily due to its bidirectional nature, which enables it consider both previous and upcoming words in a given text. The embeddings produces are often dense with information, making them well-suited for tasks like ours that require nuanced understanding of content.
Prevalence in similar research: Several peer-reviewed studies have leveraged BERT for feature extraction from textual data. For instance, QQu al. (2020) have harnessed BERT's capabilities for extracting contextualized features in various domains, showcasing its versatility and applicability. Sawhney et al. 2022) have used BERT to extract text embeddings from NFT-related tweets.
D DATA ETHICS
D.1 OPENSEA DATA COLLECTION
To obtain X handles associated with Ethereum addresses, utilize OpenSea's public API endpoint "User" Our data collection methods strictly adhere to OpenSea's 's Terms of Service and privacy policy
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According these terms, any information or data that users add their accounts, including usernames, preferred items, watchlisted collections, and other provided details, may be collected. Additionally, social media accounts such X accounts linked to the user can also be collected. It is important to note that public content may remain accessible on the internet following its deletion from an OpenSea account.
Therefore, is both possible and ethical for us to gather X handles connected to Ethereum addresses from OpenSea user profiles. However, before releasing any data collected from OpenSea, we take steps to ensure that all user data is anonymized to protect users' privacy.
By adhering to these policies and practices, we can collect valuable data for our analysis while respecting the privacy of individuals.
D.2 X DATA COLLECTION
Our collection of X interaction data is carried out ethically and in compliance with X's policies. We gather information on following relationships between handles formatted as one handle following another, as well as user profiles using the "Friends" and "User" API endpoints provided by X developers.
To ensure compliance with X's off-X matching policy 0 we are very careful when matching X accounts with Ethereum addresses. This policy restricts us to using only publicly available data for matching attempts, which includes profile information such as the account bio and publicly disclosed location, as well as the display name and @ handle. It is important to note that the display name can be changed freely by users at any time, while the @ handle is unique to each user, and is commonly referred to as the user name.
By adhering to this policy, we ensure that our approach to matching X accounts with Ethereum addresses is both ethical and effective. While we only have access to limited public information, can still achieve a high degree of accuracy in our matching attempts. However, it is important to note that due to the limitations of this approach, there may be cases where are unable to match X account with an associated Ethereum address.
To ensure that data is open-source, we closely follow X's content redistribution policies designed for academic researchers. These policies permit academic researchers associated with an academic institution and engaged in non-commercial research distribute unlimited number of Tweet IDs and/or User IDs. This enables us to share an unlimited amount of User IDs for purposes such as facilitating peer review or validating research. We have made the X following graph open-source, anonymizing all handles by substituting them with their numeric IDs in the graph. Additionally, we provide open-source BERT-handled vectors consisting solely of numerical data for X profiles, ensuring that no word information from the original profiles is disclosed.
E ON-CHAIN DATA COLLECTION
Though many previous works utilized third-party API such as Etherscan blockchain explorer API to
collect Ethereum transaction records (Lin et al. 2020 2021 2022: Wang al. (022). we choose to instantiate a local Ethereum Virtual Machine (EVM) to acquire complete Ethereum transaction records. This approach ensures we obtain thorough dataset encompassing all on-chain activities without depending on third-party platforms.
The first step involves setting up an Ethereum node on local machine. We use Geth which synchronizes with the Ethereum blockchain network. We choose Geth's full node type that provides a more comprehensive dataset but requires substantial storage space as downloads the entire blockchain.
After successfully establishing the local node, we start the process of extracting data. Ethereum transactions are packaged into blocks that are appended to the Ethereum blockchain. These blocks contain various pieces data, including the sender's address, receiver's 's address, value transferred,
http:////deeellppe...ooo//////// en deve ptteeveellprr-teespppllllll. httpss///eth.eehhreem
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gas used, timestamp, and more. Using the local geth node's API, we parse these blocks and extract
relevant transaction data. One example is below:
For example, when parse block, the data structure may resemble the following:
"number": "4370000",
"hash": "Ox5eaa2...",
"parentHash"; "Oxieaa3...".
"nonce": "0x0166f722471100""
"sha3Uncles": "Oxldcc...""
"logsBloom": "Ox...",
"transactionsRoott. "0x9a<f1f...""
"stateRoot": "0x5f5f8f...",
"miner": "0x2a6...",
"difficulty": "2262461398322224""
"totaaDiificulty": "I57735592994933331
"size": "13606",
"extraData": "0x74657374",
"gasLimit": "6712388",
"gasUsed"; "21000",
"timestamp": "1502133250",
Itransactions: ons" [ },
"uncles"; [...
The "transactions" field in block on the Ethereum blockchain contains an array of transaction objects. each representing specific transaction that occurred within that block. These transaction objects typically include critical information such as the sender's address ("from")), the recipient's 's address ("to"), the value transferred ("value"), and the gas used ("gas"), among other details. Additionally, the block's "timestamp" field provides us with the time when the block was mined and the "block_ number" is the unique identifier assigned to that specific block by the network.
By parsing this data, we can construct a comprehensive record of all transactions that have occurred on the Ethereum blockchain, enabling to analyze patterns in on-chain activities and link them with off-chain data to gain deeper insights into the behavior of Ethereum users.
To obtain a comprehensive dataset for our study, we continuously synchronize the local EVM with the Ethereum blockchain network, ensuring that we collect up-to-dare data. This method facilitates the tracking of evolving patterns in on-chain activities and helps maintain expansive and current dataset.
For this study, we filter the Ethereum transaction data from March 2022 August 2022 focus specific timeframe. We then narrow down the dataset by filtering out all NFT transaction records using the transaction hash value as a filter criterion By doing so, we can efficiently extract all relevant transaction records related to NFT trading during this period for further analysis.
F MATCHING DATA COLLECTION
To collect matching data between Ethereum addresses and OpenSea user profiles, we utilize OpenSea's 'User' endpoint API. This API takes an Ethereum address as input and returns the associated OpenSea user profile information, if available. By using this API, we can efficiently match Ethereum addresses with OpenSea user profiles and extract relevant data for our analysis. It is important to note that this process relies on the accuracy and completeness of the data provided by OpenSea, which may be subject to limitations errors.
For example, when we use the Ethereum address 04F50000774550000000//9900044440 to query the API, the call looks like this:
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The API then returns response that contains the corresponding OpenSea user profile information, including any connected X accounts, applicable. The response as follows here:
"username": "xx..."
"account":
"user":
"username": "xx..."
),
" sprofile_img_urly urr"" http:://tt//e.eeccc.oo///////
aadrress: 00000008550000115559988887222333
" "config": ""
"currencies": O,
"profile_ine serriilee_mage__e url": /htpp:://..aacc...o//gge
"website_url= "",
"x "X_username"; "xx..."
In this study, we take great care anonymize OpenSea user's connected X account information to protect their privacy. It is important to note that the linkage between a X account and an OpenSea user requires verification from OpenSea, which provides us with ground-truth information for our matching attempts.
By examining the response data, specifically the 'X username' field, we are able extract matched X account associated with given Ethereum address. This process enables to establish reliable mappings between accounts and Ethereum addresses while preserving user anonymity.
Overall, our approach respects user privacy while providing us with accurate data for our analysis of the relationships between X users and the NFT market on the Ethereum blockchain.
G OFF-CHAIN DATA COLLECTION
During the early phases of our investigation, we embarked on comprehensive assessment of various potential external data sources, including X. Instagram, Facebook, Discord, and Reddit. Our selection criteria for these platforms hinged on their user activity levels and their apparent relevance to Ethereum interactions. Of these platforms, X stood out due to its heightened engagement with discussions surrounding Ethereum and NFTs. Additionally, X boasts a developer-centrie API that simplifies the extraction of user relationships, particularly "following" connections. Beyond mere connections, X's extensive user profile data serves gold mine, providing broad and deep spectrum of information pivotal to our analysis. In juxtaposition, platforms such as Instagram. Facebook, Discord, and Reddit either do not offer detailed APIs or fundamentally more reserved in their user interactions, making data procurement and analysis more cumbersome.
Another compelling factor underpinning selection was the availability of an API from OpenSea, the premier NFT marketplace. This API provision enabled us to establish direct and verified linkages between X accounts and their respective OpenSea profiles. Such integration between on-chain and off-chain data but also reinforced our conviction in centering our efforts on X.
only bridged the gap
To accumulate X's follower-following relationships, we leverage X's Developer API in this study 12 We begin by using the 'User' endpoint to fetch user profile details using the account user name. Then. for each retrieved user, we obtain their followers and the accounts they are following through the 'Followers' and "Friends" endpoints, respectively.
The 'Followers' endpoint returns list of user objects for every user following the specified user, while the 'Friends' endpoint returns a list of user objects followed by the specified user. By extracting this information, we construct graph of the follower-following relationships.
htttps://deeellgae http:/ppp......ooo//o/o/ccccaa//
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It is important note that we strictly adhere to X's API usage policies ensure the privacy and security of user data. To maintain anonymity when open sourcin the collected data, we anonymize X user names with their numerical IDs.
This method offers several advantages as it provides a wealth of additional information, including publicly shared profile information, tweets, retweets, and likes, which could provide valuable features for our future analysis. Despite the limitations imposed by the API rate limits, we were able to collect substantial off-chain data that forms critical part of our dataset.
By incorporating this off-chain data collection method, we can gain a more comprehensive under- standing of Ethereum address activities by adding essential layer of social network context the on-chain transactional data.
H STATISTICAL ANALYSIS
In this section, we conduct a detailed analysis of the statistical results for F8 (Average neighbor degree) measurement of both X-matched Ethereum addresses and wash-trading Ethereum addresses to gain deeper insights into their behavior on the Ethereum network.
H.1 NFT MINTING PROCESS
After observing an unusually high average neighbor degree among Ethereum addresses that match with X accounts, we conducted a thorough investigation understand the root causes. Our findings indicate that this phenomenon is largely driven by the process of minting Non-Fungible Tokens (NFTs) on the Ethereum blockchain (Ghelani 2022).
To mint NFTs on Ethereum, a smart contract is typically deployed to manage the minting process, and each user who mints an NFT interacts with this contract, resulting in a transaction being added to the blockchain. The unique nature of NFT minting means that large number of Ethereum addresses (users) interact with a relatively small number of smart contracts. As a result, these smart contracts, which are frequently used by Ethereum addresses matched to X accounts, have significantly higher neighbor degree, causing the overall average neighbor degree for this subset of Ethereum addresses to rise.
Additionally, many of these X-matched Ethereum addresses belong to artists or NFT creators who highly active in the community, including minting NFTs and interacting with smart contracts. This increased activity leads to a higher frequency of transactions and drives up their neighbor degree even further.
Our discovery highlights the critical role of integrating off-chain information to gain a comprehensive understanding on-chain activities. It underscores how social media influence and engagement can have a significant impact on on-chain behavior, further emphasizing the value of our integrated dataset exploring such dynamics.
H.2 WASH TRADING PATTERNS
When comparing X-matched Ethereum addresses with wash-trading Ethereum addresses, we observe a distinctly lower average neighbor degree (F8 feature) for the latter. This is because that Ethereum addresses, by nature, engage in a more closed network of transactions (von Wachter et al. 2022).
In typical wash trading scheme, single entity coordinated group of entities engage in deceptive transactions where no actual change ownership occurs. We list some typical wash trading patterns in Figure III
We explain the four patterns presented in this figure follows:
pattern (a), two Ethereum addresses engage in a circular flow funds, where one address sends Ether to the other address, which then sends it back to the first address. This cycle of transfers creates the illusion of high trading volume while no actual trades are taking place.
In pattern (b), three Ethereum addresses form triangle of transfer activities, where each address sends Ether to another address in the cycle. Again, this creates the appearance of high trading volume without any actual trades.
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Pattern (c) involves three Ethereum addresses participating in cycle of transfers, where one address sends Ether to another address, which then sends to a third address, and finally, the third address sends it back to its sender. This type of pattern is similar to pattern (b). The purpose of this pattern is to create the illusion of high trading activity without any actual trades taking place.
Finally, pattern (d) is variant of pattern (b), where four Ethereum addresses participate in circular flow of funds. Again, this creates the illusion of high trading activity without any real trading happening.
A
B
A
B
A
B
C
c
D
(b)
(c)
Figure III: Four typical wash trading patterns. Nodes B, C and represent Ethereum addresses involved in a cycle of transfer activities.
We can see that the transaction patterns of wash-trading addresses result in a lower average neighbor degree these addresses tend to transact exclusively amongst themselves, limiting their interactions with broader network of nodes and smart contracts discussed in Section H.l As result, they form smaller, tightly-knit network of interconnected addresses.
Understanding this pattern is crucial for detecting and preventing wash trading activities on the Ethereum network. Incorporating these insights into our dataset strengthens our understanding of Ethereum network dynamics and helps to develop more efficient detection models to combat illicit activities such as wash trading.
I ETHEREUM LINK PREDICTION
1.1 FEATURE ENGINEERING
Our methodology for Ethereum link prediction involves utilizing cight structural features derived from Ethereum addresses, outlined in Section 4.1 of the main paper. These features serve the primary input for our modeling approach and help us to accurately predict links between Ethereum wallets.
For Deep Walk and Node2Vec models, we generate features through a combination of random walks the transaction graph and semantic features associated with matched X accounts. This comparative study allows us to assess the degree of enhancement in the models' predictive power when off-chain social information is incorporated.
For the rest Graph Neural Network (GNN) models, a similar approach is followed. We gauge the effectiveness of the models when trained structural features alone and contrast it with their performance when trained on both structural and corresponding X account semantic features. This comparison serves to underline the value added by off-chain data in on-chain prediction tasks.
The training of each model yields node embeddings, which essentially represent a compressed form of the nodes. Each model's performance averaged across five separate experiments, with the standard deviations also being reported to capture the variability in outcomes.
1.2
MODEL DESCRIPTIONS
In this section, we introduce the models that we utilized as baselines for the Ethereum link prediction task.
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Deep Walk is popular graph embedding algorithm that uses random walks to generate node embeddings homogeneous network. In DeepWalk, each node is treated as word in corpus, and a sequence of nodes obtained through random walks is used to learn vector representations of the nodes.
DeepWalk (Perozzi et al., 2014):
GGNN (Li et al. 2013): Gated Graph Neural Network (GGNN) is a type of recurrent neural network designed for graph data. It uses gated recurrent units (GRUs) to update node states in an iterative fashion and can be applied to graphs with different structures and sizes.
Node2Vec is another graph embedding algorithm that generates node embeddings using a biased random walk approach. It introduces two parameters that control the random walk process: the return parameter, which determines the likelihood of returning to the previous node, and the input parameter, which balances between breadth- fraath-first..m and depth-first exploration.
Vec (Grover Leskoved 2016);
Graph Convolutional Networks (GCN) Kiipf & Welling 2016): GCN a popular deep learning- based model for graph data that uses convolutional neural networks to learn node embeddings. GCN operates on a fixed graph structure and uses the same weights across all nodes to enable efficient computation.
GAT (Veličković et al., 2017): Graph Attention Networks (GAT) is a deep learning- based model for graph data that uses attention mechanisms to learn node embeddings. GAT computes attention coefficients based on the features of nodes and their neighbors and aggregates information from neighboring nodes using weighted sum.
GraphSAGE (Hamilton et al. 2017): GraphSAGE is variant of the GCN model that generalizes well to graphs with different structures and sizes. It learns node embeddings by aggregating information from the local neighborhood GraphSAGE places more emphasis the individual nodes themselves, enabling it to better capture the diversity and complexity of real-world graph data.
each node using trainable function. Unlike GCN,
TAGCN (Du et al., 2017): Topology Adaptive Graph Convolutional Networks (TAGCN) is graph embedding model that extends the GCN model by incorporating adaptive weights into the convolutional operation. These adaptive weights are learned from the graph topology and enable TAGCN to adjust its parameters based on the degree of each node, allowing it to better capture the local structure of the graph. This makes TAGCN more effective modeling complex networks with diverse degrees and enhances its performance on tasks such as node classification, link prediction, and clustering.
APPNP (Gasteiger et al. 2018): Approximate Personalized Propagation Neural Predictions (APPNP) is semi-supervvied learning algorithm for graph data that improves upon the propagation scheme GCN by incorporating ideas from personalized PageRank. APPNP uses a modified version of PageRank to propagate node representations across the graph and make predictions, enabling to capture local and global information in the network and achieve improved performance downstream tasks.
Cluster-Gri GCNN is a scalable variant of the GCN model that partitions large graphs into clusters and performs GCN operations each cluster separately. It allows for efficient parallelization and can handle much larger graphs than traditional GCN models.
Cluster-GCY (Chiang al., 2019)
DAGNN Liu et al. 2020): DAGNN is deep learning model that operates on directed acyclic graphs (DAGs). outperforming conventional GNNs by leveraging the partial order strong inductive bias besides other suitable architectural features.
GATv2 is improved version of the GAT model that addresses the static attention problem of the standard GAT layer by allowing every node to attend to any other node, unlike in the standard GAT where the ranking of attended nodes is unconditioned on the query node.
GATv2 [Brody al. 2021):
I.3 HYPERPARAMETER SETTINGS
In this section, present list of the hyperparameters and final hyperparameters used in each baseline model for Ethereum link prediction. To ensure consistency across all models, we set the output node embedding size to 128 and the learning rate to 0.001.
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Random-walk based model settings. We refer to the original literature of DeepWalk (Perozzi et al. 2014) and Vede2Vec (Grover & Leskovec 2016) to guide the hyperparameters tuning. We list the hyperparameters tried in Table VIII
Table VIII: Random-walk based models' hyperparameters for Ethereum link prediction.
Model
Walk Length
Num of Walks
Window Size
p
DeepWalk
[20, 40, 50, 60, 70, 80]
[10, 20, 30, 40]
[3. 5, 10]
N.A.
N.A.
Vece2Vec
[20, 40, 50, 60. 70, 80]
[10, 20, 30, 40]
[3, 5, 10]
[0.25, 0.5,
[0.5, 1. 2]
Through experimentation. we have found that a walk length of 20 and a window size of 5, with 40 walks per node, produce optimal results for DeepWalk. For our implementation of 2ode2 we set hyperparameters p 0.5 and y = 2, while keeping all other hyperparameters the same as for DeepWalk. These hyperparameter settings guide the random walk to explore the graph more efficiently, resulting in superior performance.
Baseline GNN model settings. Following the suggestions from the recent literature, we employ a three-layer architecture for all graph neural network (GNN) models, which allows us to capture sufficient information from the graph while preventing overfitting. The optimization of the models' hyperparameters is accomplished through a comprehensive grid-search strategy, drawing guidance from the respective original papers and code repositories of GGNN (Li et al. 2015), GCN (Kipf & Welling 2016), GAT (Veličković et al,,2017), GraphSage (Hamilton et al.
2017). TAGCN (Du
al. 2017 APPNP (Gasteiger et al. 2018). Cluster-GCN (Chiang et 2019 DAGNN (Liu et al.
2020), and GATv2 [Brody et al. 2021
We list all parameters we implemented on each GNN model
Table
Table IX: GNN models' hyperparameters for Ethereum link prediction.
Model
Hidden Dimensions
Dropout Rate
Num of Heads
Negative Slope
GGNN
[16, 32, 64, 128]
[0.1, 0.2, 0.5]
N.A.
N.A.
12, 3, 5]
GCN
32, 64, 128]
10.1,022 0.5]
N.A.
N.A.
N.A.
GAT
[16.32, 64, 128]
[..1, 0.5]
[3, 4,
N.A.
N.A.
GraphSage
[16, 32, 64, 128]
[0.1, 0.5]
N.A.
N.A.
N.A.
TAGCN
[16, 64, 128]
0..1,
N.A.
N.A.
N.A.
APPNP
[16, 32, 64, 128]
[0.1, 0.5]
N.A.
N.A.
3,51
Cluster-GCN
[16, 32, 128]
10.1, 0.5]
N.A.
N.A.
N.A.
DAGNN
[16, 32, 64, 128]
[0.1, 0.2, 0.5]
N.A.
N.A.
[1.2,3]
GATv2
[16, 64, 128]
0.2, 0.5]
4,
[0.01, 0.2, 0.5]
N.A.
After conducting experiments, we have determined that all GNN models perform optimally with hidden dimension 128 and dropout rate 0.1. Another model DAGNN, while sharing the same parameters as GCN, adopts A: = 1, where & represents the number hops visible by each node during training. For GAT and GATv2 Models, we set the number of heads 3. The GATv2 model further includes negative slope parameter set at 0.2, which is often used in deep learning models to address the "dying ReLU" problem by allowing for non-zero gradients even when the input is negative (L et al. 2019).
For the GGNN model, we set 5, which represents the number of steps executed during each iteration of the algorithm. These steps are used to update the hidden state of each node in the graph based on its previous state and the information from neighboring nodes.
On the other hand, for the APPNP model, propagation step count (also k) of 3 is used. This parameter is used to dynamically control the amount of information that propagates across the graph during each iteration of the algorithm when computing personalized page rank. A larger l will result in more information propagating across the graph and potentially larger improvements in performance, but also increased computational complexity.
1.4 ETHEREUM LINK PREDICTION FOR NON-MATCHING ETHEREUM ADDRESSES
Ethereum link prediction can help monitor transaction trends of Ethereum. However, previous works only research on Ethereum dataset (Lin et al. 2021) (Lin al. 2022). Therefore, we aim compare
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For the validation and test sets, we choose positive edges from the previously partitioned validation and test sets and ensure that both nodes in a validation or test edge are also present in the training graph. Also, positive validation and test edges do not contain any Ethereum addresses with matching Published as a conference paper at ICLR 2024
the performance between scenarios: one with the addition of X features and one without, especially for those Ethereum addresses without X matching. We employ five baseline models: GCN (Kipf & Welling 2016). GAT (Veličković al. (2017), APPNP (Gasteiger et al. 2018). TAGCN (Du et al.
2017]. Cluster-GCN (Chiang et al. 2019).
Implementation details. Firstly, We divide all Ethereum graph edges into training. validation, and test sets, following 70/10/20 ratio based on edge timestamps. We then remove the validation and test edges and choose the largest connected subgraph from the remaining edges to constitute our training graph. This process ensures that we only use old links to predict new ones. Next, we random choose the same number of edges as in Section 4.2 as positive training edges. And we make sure these edges do not contain Ethereum addresses which have matching X accounts. For balance, we generate an equal number of negative training edges by selecting a non-existent link from each Ethereum address within the positive training edges. Again, we make sure these negative training edges do not contain Ethereum addresses which have matching X accounts.
accounts.
For every positive edge within the validation and test sets, randomly choose a non-existent link stemming from the same Ethereum address. Again, we make sure these negative validation and test edges do not contain Ethereum addresses which have matching X accounts. With this framework, we execute two experiments: one that incorporates X features and one that does not. Our goal is to compare the results of these two scenarios to determine the influence of X data on the analysis of Ethereum activities.
Evaluation metries. We the following four widely used metrics (Chen et al. 2020), to evaluate the performance of experiments in a comprehensive way: (1) AUC-ROC, (2) Precision, (3) Recall, and (4) Fi-score. Note that, for all the experiments we repeat 5 times and report the average performance with standard deviation.
Table X: Results for Ethereum link prediction, with t. and J. indicating performance increase, maintaining and decrease, respectively.
Method
Without Features
With X Features
AUC-ROC
Precision
Recall
Fl
AUC-ROC
Precision
Recall
Fl
GCN
0.84
0.76.10.01
0.75110.01
0.75.10.00
0.8410.00 (t)
0.76 0.00 (1)
0.75 (L) 0.7610.01 (t)
GAT
0.78+0.02
0.7240.01
0.68++0.3
0.70-0002
0.85+0.01
0.76-0.00
0.79-0.02 (f)
0.77+0001 (f)
TAGCN
0.84.10.00
0.81.10.0
0.71.10.00
0.76.10.00
0.85 (t)
0,80 0.80_0.01 (0)
0.7410.01 (?)
0.77100.0 (f)
Cluster-GCN
0.88+0000
0.81++0.00
0.78+0.01
0.80-0001
0.90+0.00
0.82-0.00
0.82-0.1 (
0.82+0000
APPNP
0.84.10.01
0.80.10.00
0.70.10.01
0.74.10.00
0.87.10.00 (f)
0.79 (
0.77.0.00
0.78.0..0 (f)
Discussion. Results shown in Table X demonstrate the superiority of GNN that leverage both graph structures and X features compared those that rely solely on graph structures. Notably, incorporating features from matched X accounts significantly improves the performance of non- matching Ethereum addresses' link prediction across all models. For instance, GAT exhibits a substantial 7% increase in AUC-ROC when integrating X features, while APPNP shows an 7% surge in recall. This enhancement is attributable to the unique insights obtained from X data, which reveal behaviors and characteristics of Ethereum addresses not discernible from Ethereum transaction data alone. Additionally, incorporating X data enables an effective utilization of the message passing mechanism in GNNs in the dataset. These findings underscore the potential of our dataset, which benefits analysis of not only Ethereum addresses with matching X accounts but also those Ethereum addresses lacking external information.
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J DETECTION OF WASH-TRADING ADDRESSES
J.1 FEATURE ENGINEERING
In our analysis for wash-trading address detection, we incorporate a set of eight structural features identified in our statistical analysis section. These structural features act as primary input for Ethereum addresses.
For DeepWalk Walk and Node2Vec, we explore the effectiveness of features generated through random walks alone as well as a combination of random-walk features and semantic features derived from associated X accounts. This approach enables evaluate the impact of incorporating off-chain information on the models' performance.
For other Graph Neural Network (GNN) models, we similarly compare the performance when using only the structural features against combined set of structural and semantic features, with the semantic features being extracted from the respective X accounts. The aim here to ascertain the extent to which the inclusion of off-chain social information can improve the accuracy of wash-trading address detection.
The node embeddings, which serve condensed representation of the nodes, are derived from the trained models. It's worth noting that, for each model and experimental setting, we report the average results over five experiments, along with the associated standard deviations.
J.2 MODEL DESCRIPTIONS
In this section, we introduce the models that utilized as baselines for detecting wash-trading Ethereum addresses. All of the models were already introduced in Section
J.3 HYPERPARAMETER SETTINGS
In this section, we present list of the hyperparameters and final hyperparameters used in each baseline model for wash-trading addresses detection. To ensure consistency across all models, we set the dimension of the output node embeddings to 2 as there are two classes of addresses: wash-trading address and normal address. And we set the learning rate to 0.001.
Random-walk based models settings. We refer to the original literature of DeepWalk (Perozzi et al. 2014) and Node2Vec (Grover & Leskovec 2016 to guide the hyperparameters tuning. We list the hyperparameters we tried in Table XI
Table XI: Random-walk based models' hyperparameters for wash-trading addresses detection.
Model
Walk Length
Num of Walks
Window Size
WelkWalk
[20, 40, 80]
[10, 20, 30, 40, 50]
[3, 5, 10]
N.A. 10.25, 0.5, 1]
N.A. [0.5, 1, 2]
Vece2Vec
[20, 40, 80]
[10, 20, 30, 40, 50]
[3, 10]
Through experimentation, we have determined that a walk length of 20 and a window size 5, with 40 walks per node, produce optimal results for DeepWalk, For our implementation of Node2Vec, we set hyperparameter p = 0.5 and q 2, while keeping all other hyperparameters the same as those used in DeepWalk.
Baseline GNN model settings. Following the suggestions from the recent literature, we employ three-layer architecture for all graph neural network (GNN) models, which allows us to capture sufficient information from the graph while preventing overfitting. We employ grid-search strategy, informed by the recommendations and code repositories of several baseline models, including GGNN (Li et al. 20155), GCN (Kipf & Welling 2016), GAT (Veličković et al. 2017). GraphSage (Hamilton al. 2017h, APPNP (Gasteiger et al. 2018). and GATv2 (Brody et al. 20211. We list the hyperparameters in the Table(XI
After experimenting, dropout rate 0.1. For GAT and GATv2 Models, we set the number of heads to 3. The GATv2 model further includes negative slope parameter set at 0.2, which is often used in deep learning models
have decided that all GNN models share hidden dimension 128, and
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Table XII: GNN models' hyperparameters for Ethereum link prediction.
Model
Hidden Dimensions
Dropout Rate
Num of Heads
Negative Slope
d:
GGNN
[16, 32, 64, [28]
[0.1, 0.2, 0.5]
N.A.
N.A.
3, 51
GCN
[16, 32, 64, 128]
[0.1, 0.2, 0.5]
N.A.
N.A.
N.A.
GAT
[16. 64, 128]
[0.1, 0.5]
[3, 4,4, 5]
N.A.
N.A.
GraphSage
[16, 64, 128]
[0.1,
N.A.
N.A.
N.A.
TAGCN
[16, 32, 64, 128]
[0.1,
N.A.
N.A.
N.A.
APPNP
[16, 32, 64, 128]
[0.1,
N.A.
N.A.
3,
Cluster-GCN
[16, 32, 64, 128]
10.1, 0.5]
N.A.
N.A.
N.A.
DAGNN
[16. 32, 128]
0.2.0.2.0.5.
N.A.
N.A.
[1,2,3] 3]
GATv2
[16, 32, 64, 128]
[0.1. 0.2, 0.5]
[3. 33
[0.01. 0.2, 0.5]
N.A.
to address the "dying ReLU" problem by allowing for non-zero gradients even when the input is negative (Lu al. 2019).
For the GGNN model, we set k = 5, which represents the number of steps executed during each iteration of the algorithm. These steps are used to update the hidden state of each node in the graph based on its previous state and the information from neighboring nodes.
For GAT and GAIv2 Models, we set the number of heads to 3. The GATv2 model further includes negative slope parameter set at 0.2.
On the other hand, for the APPNP model, a propagation step count (also k)) of used. This parameter is used to dynamically control the amount of information that propagates across the graph during each iteration of the algorithm when computing personalized page rank. larger will result in more information propagating across the graph and potentially larger improvements in performance, but also increased computational complexity.
Overall, the tuning of these and additional parameters is crucial for achieving robustness and accuracy the detection wash-trading Ethereum addresses.
K
MATCHING LINK PREDICTION
K.1 FEATURE ENGINEERING
Feature engineering plays a crucial role in our analysis, allowing us to distill complex information into tractable format suitable for machine learning models. For Ethereum addresses, extract of structural features. These structural features encapsulate transaction characteristics of Ethereum addresses, capturing essential aspects of their on-chain activities.
When it comes to X accounts matched with Ethereum addresses, we supplement the same structural features Ethereum addresses with additional semantic features, extracted from the X account data, The semantic features add rich layer of off-chain information, capturing nuanced aspects of users' online social behavior.
For X accounts that do not have matched Ethereum address, preserve the dimensionality of the feature space by appending an eight-dimensional zero-vector to the existing structural features. This maintains consistency in the input feature size across all nodes, thus making it suitable for the training of GNN models. As a result, nodes in this training share an input vector of 16 dimensions.
After training, we extract node embeddings from our models. Each embedding is compact repre- sentation node, encapsulating the node's 's position and connectivity within the network. For an edge linking nodes. we generate the edge embedding by concatenating the respective two node embeddings. This resultant edge embedding carries combined information about both linked nodes, providing comprehensive feature set for edge-level analysis.
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K.2 MODEL DESCRIPTIONS
In this section, we introduce the models that we utilized as baselines for prediction matching links between Ethereum addresses and X accounts. As some of the models were already introduced in we will only introduce the new models used in this experiment.
Section
HeN2Vec (Fu et al. y017): HIN2Vec graph embedding model designed specifically for heterogeneous information networks (HINs). uses combination of meta-path-ban random walks and skip-gram models to learn distributed representations of nodes in a HIN.
R-GCN (Schlichtkrull et al. 2018): Relational Graph Convolutional Networks (R-GCN) is variant of the GCN model that can operate on multi-relational graphs with varying edge types. uses different weight matrices for each relation in the graph to enable edge-type-specific feature transformation and learning.
K.3 HYPERPARAMETER SETTINGS
For consistency, we maintain the same hyperparameter setting for this matching link prediction as used in our Ethereum link prediction model for all the models presented in this study. In particular, all models share common output node embedding size of 1 128, which determines the size of the learned representation for each node in the graph. Additionally, learning rate of 0.001 is used across all models to control the rate of learning during training. These hyperparameters were chosen based on experimentation and validation performance on the Ethereum dataset, and they are kept consistent throughout our analysis for fair comparison of the different models.
For the random-wall based graph embedding models DeepWall (Perozzi al., 2014), Node2Vec (Groerr & Leskovec 2016), and HIN2Vec (Fu et al.) 2017). we set walk length of 20 and a window size 5, denoting length of the random walk sequences and the window of context for prediction, respectively. Additionally, we set 40 walks per node to ensure adequate sampling of the graph. Specifically for Node2Vec, we set p = 0.5 and q = 2, respectively, controlling the random walk's likelihood revisit a node and explore undiscovered parts of the graph.
For our GNN-based models, we configure them with three layers, a hidden layer dimension of 128. and dropout rate of 0.1. R-GCN, while sharing these parameters, distinctly incorporates three types of relational edges to capture the heterogeneity of our constructed graph.
The DAGNN model, keeping the GCN parameters consistent, specifically uses hyperparameter 1. This denotes the number hops each node perceives during training. With 1, the model takes into account only the immediate neighbors of each node for updating its representation.
For the GGNN model, we adopt a setting of l = 5, representing the steps taken during each iteration. These steps facilitate the update of each node's 's hidden state based prior states and input from adjacent nodes.
For both the GAT and GATv2 models, determine the number of heads to be 3. Specifically, the GATv2 model incorporates an additional negative slope parameter, set at 0.2.
Conversely, for the APPNP model, use propagation step count (denoted k) of 3. This parameter dynamically dictates the volume of information flowing across the graph during each iteration when computing the personalized page rank. A higher k potentially enhance performance due to increased information flow but comes at the expense of heightened computational demand.
L URL TO DATASET
We deploy our dataset on a GitHub repository:
httpps//ggthubbb.o/////rrtt////t
Graph
M DATASET DOCUMENTATION
The EX-Graph dataset a collection on-chain Ethereum transaction records and off-chain X followe data, arranged graphs in DGL (Deep Graph Library) format. The dataset
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is suitable for various research tasks, including Ethereum link prediction, wash-trading addresses detection, and matching link prediction.
M.1 INTENDED USE
This dataset is designed for researchers and developers who are interested in exploring the behavior of Ethereum transactions from both on-chain and off-chain domains, as well as the relationships between Ethereum addresses and X users. The dataset can facilitate research studies various topics, including fraud detection, transaction analysis, and social network analysis.
M.2 ACCESSING THE DATA
Due to its large size, the EX- EX-Graph dataset is hosted Google Drive for easy access and download. The dataset comprises these main parts:
X Graph: Represents accounts and their relationships.
Ethereum Graph: Describes Ethereum addresses and their transaction history.
Ethereum Graph with X features: A DGL graph that integrates Ethereum addresses, their transactions, and features from both Ethereum and matched X accounts. Used for Ethereum link prediction tasks.
Wash-trading Addresses Detection Training Graph: The training graph used for detecting wash-trading addresses.
Wash-trading Addresses Detection Validation Graph: The validation graph for the same task.
Wash-trading Addresses Detection Test Graph: The test graph for the task.
Matching Link Prediction Graph: A DGL graph utilized for the matching link prediction task.
To access the data, please follow the links provided in the GitHub README file and download the relevant files from Google Drive.
M.3 CODE FOLDERS
The repository contains three code folders that correspond to the different use cases of the EX-Graph dataset:
Ethereum link prediction: This folder contains the source code for using the dataset for Ethereum link prediction.
Wash trading address detection: This folder contains the source code for using the dataset for detecting wash-trading addresses.
Matching link prediction: This folder contains the source code for using the dataset for matching link prediction.
M.4 DATA FILES
In addition the code folders, the repository includes two data files:
X handles (converted to numerical id for anonymization) with their matching Ethereum addresses from OpenSea: This file contains the X handles of various users and their corresponding Ethereum addresses.
Wash-trading transaction records collected from Dune: This file contains records of wash-trading transactions collected from Dune
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Published as a conference paper at ICLR 2024
M.5 LICENSE
The EX-Graph dataset is released under the Creative Commons Currbution-oonCommerriia Alike (CC BY-NC-SA) license. This means that anyone use, distribute, and modify the data for non-commercial purposes as long as they give proper attribution and share the derivative works under the same license terms.
N AUTHOR STATEMENT
As authors of the EX-Graph dataset, we hereby declare that we assume full responsibility for any liability or infringement of third-party rights that may come up from the use of our data. We confirm that we have obtained all necessary permissions and/or licenses needed to share this data with others for their own use. In doing we agree to indemnify and hold harmless any person or entity that may suffer damages resulting from our actions.
Furthermore, we confirm that our EX-Graph dataset is released under the Creative Commons Sam.roo-oooooommraaa---Saeeeeeee
(CC BY-NC-SA) license. This license allows anyone to use, distribute, and modify our data for non-commereial purposes as long they give proper attribution and share the derivative works under the same license terms. We believe that this licensing model aligns with our goal of promoting open access to high-quality data while respecting the intellectual property rights of all parties involved.
HOSTING PLAN
After careful consideration, we have chosen to host our code and data on GitHub. In addition, deploy website leaderboard for researchers submit their experiment results the EX-Graph we propose. Our decision is based on various factors, including the platform's ease of use, cost-effectiveensss, and scalability. We understand that accessibility is key when it comes data management, which is why we will ensure that our data is easily accessible through a curated interface. We also recognize the importance of maintaining the platform's 's stability and functionality, and as such, we will provide the necessary maintenance to ensure that it remains up-to-date, bug-free, and running smoothly.
At the heart of our project is the belief in open access to data, and we are committed to making our data available those who need As part of this commitment, we will be updating our GitHub repository regularly, so that users can rely on timely access to the most current information. We hope that by using GitHub as our hosting platform, we can provide sser-friendly and reliable solution for sharing our data with others.
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Reviews:
[{"review_id": "atg9Fl2wLS", "reviewer": "Reviewer_SzMQ", "summary": "The paper delves into the analysis of Ethereum activities, specifically examining the influence of Twitter data on these activities. Through experiments, the authors compare the outcomes of Ethereum link predictions and wash-trading address detections with and without the integration of Twitter features. Their findings suggest that incorporating Twitter data can significantly enhance the performance of various graph-based models in these tasks. The research leverages advanced computational tools and the ETGraph dataset, emphasizing the potential synergy between blockchain activities and social media data.", "questions": "No questions.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper provides a new dataset for graph representation with Ethereum blockchain and Twitter data. As there are already existing datasets for the separated dataset, the combined dataset with opensea may be considered a contribution. The authors experimentally proved that combining the dataset improved the performance of Ethereum link prediction and wash trading detection using various existing methods. \nThe novel dataset could be used in both the blockchain and ML communities to compete with SOTA for graph learning algorithms.", "weaknesses": "As the paper suggests a new dataset, it is acknowledged that the technical novelty is not strong enough. But as the authors provide a webpage and github page to easily use the dataset, it may contribute to the ML community.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oZsco5XbiO", "reviewer": "Reviewer_AoG7", "summary": "The paper constructs a new graph dataset which uses Ethereum NFT transaction records together with Twitter social network. With the information from Twitter graph, the authors use BERT and deep walk to extract semantic and topology information, which is used as node feature for node on Ethereum graph. They conduct experiments on link prediction, wash-trading addresses detection, and matching link prediction tasks, demonstrating with the help of twitter features, various kinds of GNN methods can get performance improvements on all these tasks.", "questions": "See the weakness part.", "limitations": "", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper proposes a novel idea to link Twitter social network to Ethereum networks which can provide node features for Ethereum networks.\n2. Experiments are conducted thoroughly and prove that with Twitter features, most kinds of models can achieve better performance. Dataset statistics are provided clearly.", "weaknesses": "1. Though the title says ‘bridging Ethereum and Twitter’, the proposed dataset is focused on NFT transactions and related Twitter accounts. The author should either prove that with this dataset, GNN would have the ability to find other kinds of matching links (i.e., phish-hack EOA nodes and corresponding Twitter accounts), or modify the title to be more precise.\n\n2. In Section 3.3, the authors claim, ‘we obtain embeddings for all Twitter accounts using the DeepWalk algorithm in the Twitter graph’. Figure 2 also shows structural features given by deep walk. However, as stated in Appendix, only the handcrafted 8 features from C.1 are used.\n\n3. For task 1, the authors only use matched addresses. Since GNN can pass node features via message-passing mechanism, the semantic information is able to spread around. Whether does the semantic information benefit or harm the prediction of non-matched addresses?\n\n4. For task 3 is to predict whether one Ethereum address is connected to one Twitter account. In Appendix K, the authors describe\n\"For Twitter accounts that do not have a matched Ethereum address, we ... appending an eight-dimensional zero-vector to the existing structural features.\"\n While in Section 4.4, the negative samples are pick non-existing connections between Ethereum addresses and all their matched Twitter accounts. If the negative samples are $\\{e_k=(v_i, v_j)| v_i \\in V_\\text{eth}, v_j\\in V_{\\text{twitter with profile}}, e_k\\notin E_\\text{match}\\}$, why are there Twitter accounts with no Ethereum addresses? If the negative samples are $\\{e_k=(v_i, v_j)| v_i \\in V_\\text{eth}, v_j\\in V_{\\text{twitter}}, e_k\\notin E_\\text{match}\\}$, then does the input contain bias already?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ati7b6uB0e", "reviewer": "Reviewer_hHyG", "summary": "The paper contributes a dataset, termed ETGraph, that links Ethereum addresses with Twitter accounts. The dataset is structured in the form of a heterogeneous graph and inlcudes links between pairs of Ethereum addresses that transact with each other, links between Twitter accounts that follow/are followed by each other and links between Ethereum addresses and Twitter accounts that have been matched to be owned by the same (typically, physical) entity/person. This information has been scraped from opensea and the public Twitter API. The dataset also includes information about (a relatively small number of) money-laundering addresses. \n\nIn addition, the paper also evaluates the dataset over a series of benchmark Graph Neural Network (GNN) models. Specifically, it compares whether the new dataset (with Twitter data) improves the ability of the models to predict Q1) links between Ethereum addresses, i.e., transactions, Q2) money laundering addresses, and Q3) matches between Ethereum addresses and Twitter accounts. With few exceptions, most evaluations suggest improved performance - at average levels around 6%-8% (maybe higher in some cases/lower in others) - with the new dataset in a couple of standard metrics including ROC, AUC, precision, recall and F1 scores.", "questions": "- Can you please discuss the weaknesses above?\n- Is overs-smoothing a relevant problem in the evaulations and if yes, have you taken steps to address it?\n\nMinor:\n- Figure 2: In the Feature Extraction column, we read in the first box \"Degree\"-> Structural Features. So, here, you mean that look at the degree of the node or that \"Degree\" is some kind of network like BERT and Deepwalk in the next two boxes of that column? I found this slightly confusing. Also, which structural features are extracted here since ETH addresses don't contain any further information.\n- Below Figure 2: \"The total collection accounts for 30,387 Twitter profiles\". I was confused here: why is this lower than the initial number (since, according to the previous lines more data was added) and why are Followers and Friends useful here? Friends are not discussed elsewhere if I am not wrong.\n\nTrivia: \n- should it be Twitter or X :)? \n- is it 19 billion or 190 billion as of September 23, the market value for Ethereum (page 1)? \n- please define abbreviations the first time that they are used, e.g., AUC.\n\nNote on my score: although I rated the paper as slightly above the acceptance threshold, I may decrease my score if my concerns are not addressed. I also hope that through the discussion, I will be able to increase my confidence.\n\n**Post-rebuttal**: Based on the authors' response, I raised my score to 8. I cannot remove the ethics flag, but, I believe that the authors have addressed this concern. I will not increase my confidence, since datasets is not my exact area of expertise - although, I am fairly confident that this dataset will be useful to blockchain research.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The dataset is novel and nicely connects Ethereum data to Twitter data. It involves a lot of hard work to put this dataset together and bring it to the heterogeneous graph structure. This is likely to have an impact (be useful) to researchers in the blockchain field.\n- Regarding reproducibility, the paper is transparent and I could verify the links to the provided datasets and code.\n- The evaluations indicate generally improved performance for a wide range of benchmark GNN models.", "weaknesses": "- The paper is not self-contained and a lot of things need to be assessed through the external links to the dataset or through the appendix. I am not very familiar with dataset papers, so maybe this is generally so. \n- For this reason, I cannot comment on the ethical considerations about releasing such a dataset. \n- Q2 in the evaluation seems to make no sense to me: since there are only 3 matched addresses in the dataset, why should we anticipate improved performance with this dataset? The results are, thus, questionable to me regarding Q2. \n- The insight that Twitter-matched addresses are more active in Ethereum is rather expected since Twitter is known to be popular (as the authors also confirm) by main actors of the Ethereum ecosystem. \n- On a subjective comment, I am not sure if ICLR is the right venue to maximize visibility for this type of datasets. However, Chartalist, a recent publication in NeurIPS about a related blockchain dataset seems to suggest the contrary. Also, related to my comment about ethical considerations above, the authors seem to have followed the related discussion on openreview about Charalist and have included statements in the appendix. However, I am not in a position to assess those, because I am not aware of the exact regulations regarding such datasets. For this reason, I raise a flag for ethics review below - but I would be happy to take it down if this is clarified.\n- There are some technical things that I discuss below, but these seems to be more minor in nature. As an example, the set of features degree (3 features) and neighbor (3 features) seem to be highly linearly correlated (as also suggested by their statistics in Table 3). Keeping only of them, i.e., one from the \"degree\" family, and one from the \"neighbor\" family, would potentially improve the performance of the networks. I provide a couple of more minor comments below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)