OpenRaiser/Intern-Atlas huggingface dataset:
CAAW'23: The 2nd International Cryptoasset Analytics Workshop
Authors: Bernhard Haslhofer, Friedhelm Victor, Markus Strohmaier, Jiahua Xu
Venue: WWW
Year: 2023

This half-day workshop on Cryptoasset Analytics allows researchers from different disciplines to present their newest findings related to cryptoassets. This workshop is relevant for the Web research community for two reasons. First, on a technical level, fundamental concepts of cryptoassets are increasingly integrated with Web technologies. Second, we witness the formation of socio-technical cryptoasset ecosystems, which are tightly connected to the Web. The program will feature a mix of invited talks and a selection of peer-reviewed submissions. Topics range from empirical studies, over analytics methods and tools, to case studies, datasets, and cross-cutting issues like legal or ethical aspects.
Efficient Fair Graph Representation Learning Using a Multi-level Framework
Authors: Yuntian He, Saket Gurukar, Srinivasan Parthasarathy
Venue: WWW
Year: 2023

Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research reveals that these models can learn biased representations leading to unfair outcomes. A few works have been proposed to mitigate the bias in graph representations. However, most existing works require exceptional time and computing resources for training and fine-tuning. In this demonstration, we propose a framework FairMILE for efficient fair graph representation learning. FairMILE allows the user to efficiently learn fair graph representations while preserving utility. In addition, FairMILE can work in conjunction with any unsupervised embedding approach based on the user’s preference and accommodate various fairness constraints. The demonstration will introduce the methodology of FairMILE, showcase how to set up and run this framework, and demonstrate our effectiveness and efficiency to the audience through both quantitative metrics and visualization.
Don't Trust, Verify: The Case of Slashing from a Popular Ethereum Explorer
Authors: Zhiguo He, Jiasun Li, Zhengxun Wu
Venue: WWW
Year: 2023

Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in , a leading explorer serving Ethereum’s proof-of-stake (PoS) update. According to the explorer, we find that more than 75% of slashable Byzantine actions were not slashed. Since Ethereum relies on the “stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum’s security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer’s mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.
C-Affinity: A Novel Similarity Measure for Effective Data Clustering
Authors: Jiwon Hong, Sang-Wook Kim
Venue: WWW
Year: 2023

Clustering is widely employed in various applications as it is one of the most useful data mining techniques. In performing clustering, a similarity measure, which defines how similar a pair of data objects are, plays an important role. A similarity measure is employed by considering a target dataset’s characteristics. Current similarity measures (or distances) do not reflect the distribution of data objects in a dataset at all. From the clustering point of view, this fact may limit the clustering accuracy. In this paper, we propose c-affinity, a new notion of a similarity measure that reflects the distribution of objects in the given dataset from a clustering point of view. We design c-affinity between any two objects to have a higher value as they are more likely to belong to the same cluster by learning the data distribution. We use random walk with restart (RWR) on the k-nearest neighbor graph of the given dataset to measure (1) how similar a pair of objects are and (2) how densely other objects are distributed between them. Via extensive experiments on sixteen synthetic and real-world datasets, we verify that replacing the existing similarity measure with our c-affinity improves the clustering accuracy significantly.
Hidden Indicators of Collective Intelligence in Crowdfunding
Authors: Emoke-Ágnes Horvát, Henry Kudzanai Dambanemuya, Jayaram Uparna, Brian Uzzi
Venue: WWW
Year: 2023

Extensive literature argues that crowds possess essential collective intelligence benefits that allow superior decision-making by untrained individuals working in low-information environments. Classic wisdom of crowds theory is based on evidence gathered from studying large groups of diverse and independent decision-makers. Yet, most human decisions are reached in online settings of interconnected like-minded people that challenge these criteria. This observation raises a key question: Are there surprising expressions of collective intelligence online? Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. Crowdfunding has grown and diversified quickly over the past decade, expanding from funding aspirant creative works and supplying pro-social donations to enabling large citizen-funded urban projects and providing commercial interest-based unsecured loans. Using nearly 10 million loan contributions from a market-dominant lending platform, we find evidence for collective intelligence indicators in crowdfunding. Our results, which are based on a two-stage Heckman selection model, indicate that opinion diversity and the speed at which funds are contributed predict who gets funded and who repays, even after accounting for traditional measures of creditworthiness. Moreover, crowds work consistently well in correctly assessing the outcome of high-risk projects. Finally, diversity and speed serve as early warning signals when inferring fundraising based solely on the initial part of the campaign. Our findings broaden the field of crowd-aware system design and inform discussions about the augmentation of traditional financing systems with tech innovations.
BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection
Authors: Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, Ling Liu
Venue: WWW
Year: 2023

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.
Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks
Authors: Yue Hu, Yuhang Zhang, Yanbing Wang, Daniel B. Work
Venue: WWW
Year: 2023

With the rapid development of Internet of Things technologies, the next generation traffic monitoring infrastructures are connected via the web, to aid traffic data collection and intelligent traffic management. One of the most important tasks in traffic is anomaly detection, since abnormal drivers can reduce traffic efficiency and cause safety issues. This work focuses on detecting abnormal driving behaviors from trajectories produced by highway video surveillance systems. Most of the current abnormal driving behavior detection methods focus on a limited category of abnormal behaviors that deal with a single vehicle without considering vehicular interactions. In this work, we consider the problem of detecting a variety of socially abnormal driving behaviors, i.e., behaviors that do not conform to the behavior of other nearby drivers. This task is complicated by the variety of vehicular interactions and the spatial-temporal varying nature of highway traffic. To solve this problem, we propose an autoencoder with a Recurrent Graph Attention Network that can capture the highway driving behaviors contextualized on the surrounding cars, and detect anomalies that deviate from learned patterns. Our model is scalable to large freeways with thousands of cars. Experiments on data generated from traffic simulation software show that our model is the only one that can spot the exact vehicle conducting socially abnormal behaviors, among the state-of-the-art anomaly detection models. We further show the performance on real world HighD traffic dataset, where our model detects vehicles that violate the local driving norms.
Hierarchical Deep Neural Network Inference for Device-Edge-Cloud Systems
Authors: Fatih Ilhan, Selim Furkan Tekin, Sihao Hu, Tiansheng Huang, Ka Ho Chow, Ling Liu
Venue: WWW
Year: 2023

Edge computing and cloud computing have been utilized in many AI applications in various fields, such as computer vision, NLP, autonomous driving, and smart cities. To benefit from the advantages of both paradigms, we introduce HiDEC, a hierarchical deep neural network (DNN) inference framework with three novel features. First, HiDEC enables the training of a resource-adaptive DNN through the injection of multiple early exits. Second, HiDEC provides a latency-aware inference scheduler, which determines which input samples should exit locally on an edge device based on the exit scores, enabling inference on edge devices with insufficient resources to run the full model. Third, we introduce a dual thresholding approach allowing both easy and difficult samples to exit early. Our experiments on image and text classification benchmarks show that HiDEC significantly outperforms existing solutions.
RDF Playground: An Online Tool for Learning about the Semantic Web
Authors: Bastián Inostroza, Raúl Cid, Aidan Hogan
Venue: WWW
Year: 2023

We present RDF Playground: a web-based tool to assist those who wish to learn or teach about the Semantic Web. The tool integrates functionalities relating to the key features of RDF, allowing users to specify an RDF graph in Turtle syntax, visualise it as an interactive graph, query it using SPARQL, reason over it using OWL 2 RL, and to validate it using SHACL or ShEx. The tool further provides the ability to import and explore data from the Web through a graph-based Linked Data browser. We discuss the design and functionality of the tool, its implementation, and the results of a usability study considering students from a Web of Data course that used it for lab assignments. We conclude with a discussion of these results, as well as future directions that we envisage for improving the tool.
Analyzing Distributed Medical Data in FAIR Data Spaces
Authors: Mehrshad Jaberansary, Macedo Maia, Yeliz Ucer Yediel, Oya Beyan, Toralf Kirsten
Venue: WWW
Year: 2023

The exponential growth in data production has led to increasing demand for high-quality data-driven services. Additionally, the benefits of data-driven analysis are vast and have significantly propelled research in many fields. Data sharing benefits scientific advancement, as it promotes transparency, and collaboration, accelerates research and aids in making informed decisions. The European strategy for data aims to create a single data market that ensures Europe’s global competitiveness and data sovereignty. Common European Data Spaces ensure that data from different sources are available in the economy and society, while data providers (e.g., hospitals and scientists) control data access. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) initiative is a prime example of an effort focused on data from clinical trials and public health studies. Collecting and analyzing this data is essential to developing novel therapies, comprehensive care approaches, and preventive measures in modern healthcare systems. This work describes distributed data analysis services and components that adhere to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable) within the data space environment. We focus on distributed analytics functionality in Gaia-X-based data spaces. Gaia-X offers a trustworthy federation of data infrastructure and service providers for European countries.
A History of Diversity in The Web (Conference)
Authors: Siddharth D. Jaiswal, Animesh Mukherjee
Venue: WWW
Year: 2023

The Web has grown considerably since its inception and opened up a multitude of opportunities for people all around the world for work, leisure, and learning. These opportunities were limited to western audiences earlier on, but globalization has now put almost the entire world online. While there is a growing social understanding and acknowledgment of various gender and ethnic groups in society, we still have a long way to go toward achieving equity in gender and ethnic representations, especially in the workplace. In this paper, we attempt to quantify the diversity and evenness in terms of gender and ethnicity of The WebConference participants over its 30 year history. The choice is motivated by the monumental contribution of this conference to the evolution of the web. In particular, we study the gender and ethnicity of program committee members, authors and other speakers at the conference between 1994-2022. We also generate the co-speaker network over the three decades to study how closely the speakers work with each other. Our findings show that we still have a long way to go before achieving fair representation at The WebConference, especially for female participants and individuals from non-White, non-Asian ethnicities.
RealGraph+: A High-Performance Single-Machine-Based Graph Engine that Utilizes IO Bandwidth Effectively
Authors: Myung-Hwan Jang, Jeong-Min Park, Ikhyeon Jo, Duck-Ho Bae, Sang-Wook Kim
Venue: WWW
Year: 2023

This paper proposes RealGraph+, an improved version of RealGraph that processes large-scale real-world graphs efficiently in a single machine. Via a preliminary analysis, we observe that the original RealGraph does not fully utilize the IO bandwidth provided by NVMe SSDs, a state-of-the-art storage device. In order to increase the IO bandwidth, we equip RealGraph+ with three optimization strategies to issue more-frequent IO requests: (1) User-space IO, (2) Asynchronous IO, and (3) SIMD processing. Via extensive experiments with four graph algorithms and six real-world datasets, we show that (1) each of our strategies is effective in increasing the IO bandwidth, thereby reducing the execution time; (2) RealGraph+ with all of our strategies improves the original RealGraph significantly; (3) RealGraph+ outperforms state-of-the-art single-machine-based graph engines dramatically; (4) it shows performance comparable to or even better than those of other distributed-system-based graph engines.
Social Re-Identification Assisted RTO Detection for E-Commerce
Authors: Hitkul Jangra, Abinaya K., Soham Saha, Satyajit Banerjee, Muthusamy Chelliah, Ponnurangam Kumaraguru
Venue: WWW
Year: 2023

E-commerce features like easy cancellations, returns, and refunds can be exploited by bad actors or uninformed customers, leading to revenue loss for organization. One such problem faced by e-commerce platforms is Return To Origin (RTO), where the user cancels an order while it is in transit for delivery. In such a scenario platform faces logistics and opportunity costs. Traditionally, models trained on historical trends are used to predict the propensity of an order becoming RTO. Sociology literature has highlighted clear correlations between socio-economic indicators and users’ tendency to exploit systems to gain financial advantage. Social media profiles have information about location, education, and profession which have been shown to be an estimator of socio-economic condition. We believe combining social media data with e-commerce information can lead to improvements in a variety of tasks like RTO, recommendation, fraud detection, and credit modeling. In our proposed system, we find the public social profile of an e-commerce user and extract socio-economic features. Internal data fused with extracted social features are used to train a RTO order detection model. Our system demonstrates a performance improvement in RTO detection of 3.1% and 19.9% on precision and recall, respectively. Our system directly impacts the bottom line revenue and shows the applicability of social re-identification in e-commerce.
A Dataset on Malicious Paper Bidding in Peer Review
Authors: Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang
Venue: WWW
Year: 2023

In conference peer review, reviewers are often asked to provide “bids” on each submitted paper that express their interest in reviewing that paper. A paper assignment algorithm then uses these bids (along with other data) to compute a high-quality assignment of reviewers to papers. However, this process has been exploited by malicious reviewers who strategically bid in order to unethically manipulate the paper assignment, crucially undermining the peer review process. For example, these reviewers may aim to get assigned to a friend’s paper as part of a quid-pro-quo deal. A critical impediment towards creating and evaluating methods to mitigate this issue is the lack of any publicly-available data on malicious paper bidding. In this work, we collect and publicly release a novel dataset to fill this gap, collected from a mock conference activity where participants were instructed to bid either honestly or maliciously. We further provide a descriptive analysis of the bidding behavior, including our categorization of different strategies employed by participants. Finally, we evaluate the ability of each strategy to manipulate the assignment, and also evaluate the performance of some simple algorithms meant to detect malicious bidding. The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.
Digital Twins for Radiation Oncology
Authors: James Jensen, Jun Deng
Venue: WWW
Year: 2023

Digital twin technology has revolutionized the state-of-the-art practice in many industries, and digital twins have a natural application to modeling cancer patients. By simulating patients at a more fundamental level than conventional machine learning models, digital twins can provide unique insights by predicting each patient's outcome trajectory. This has numerous associated benefits, including patient-specific clinical decision-making support and the potential for large-scale virtual clinical trials. Historically, it has not been feasible to use digital twin technology to model cancer patients because of the large number of variables that impact each patient's outcome trajectory, including genotypic, phenotypic, social, and environmental factors. However, the path to digital twins in radiation oncology is becoming possible due to recent progress, such as multiscale modeling techniques that estimate patient-specific cellular, molecular, and histological distributions, and modern cryptographic techniques that enable secure and efficient centralization of patient data across multiple institutions. With these and other future scientific advances, digital twins for radiation oncology will likely become feasible. This work discusses the likely generalized architecture of patient-specific digital twins and digital twin networks, as well as the benefits, existing barriers, and potential gateways to the application of digital twin technology in radiation oncology.
Unsupervised Event Chain Mining from Multiple Documents
Authors: Yizhu Jiao, Ming Zhong, Jiaming Shen, Yunyi Zhang, Chao Zhang, Jiawei Han
Venue: WWW
Year: 2023

Massive and fast-evolving news articles keep emerging on the web. To effectively summarize and provide concise insights into real-world events, we propose a new event knowledge extraction task Event Chain Mining in this paper. Given multiple documents about a super event, it aims to mine a series of salient events in temporal order. For example, the event chain of super event Mexico Earthquake in 2017 is {earthquake hit Mexico, destroy houses, kill people, block roads}. This task can help readers capture the gist of texts quickly, thereby improving reading efficiency and deepening text comprehension. To address this task, we regard an event as a cluster of different mentions of similar meanings. In this way, we can identify the different expressions of events, enrich their semantic knowledge and replenish relation information among them. Taking events as the basic unit, we present a novel unsupervised framework, EMiner. Specifically, we extract event mentions from texts and merge them with similar meanings into a cluster as a single event. By jointly incorporating both content and commonsense, essential events are then selected and arranged chronologically to form an event chain. Meanwhile, we annotate a multi-document benchmark to build a comprehensive testbed for the proposed task. Extensive experiments are conducted to verify the effectiveness of EMiner in terms of both automatic and human evaluations.
Modeling Dynamic Interactions over Tensor Streams
Authors: Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai
Venue: WWW
Year: 2023

Many web applications, such as search engines and social network services, are continuously producing a huge number of events with a multi-order tensor form, {count;query, location, …, timestamp}, and so how can we discover important trends to enables us to forecast long-term future events? Can we interpret any relationships between events that determine the trends from multi-aspect perspectives? Real-world online activities can be composed of (1) many time-changing interactions that control trends, for example, competition/cooperation to gain user attention, as well as (2) seasonal patterns that covers trends. To model the shifting trends via interactions, namely dynamic interactions over tensor streams, in this paper, we propose a streaming algorithm, DISMO, that we designed to discover Dynamic Interactions and Seasonality in a Multi-Order tensor. Our approach has the following properties. (a) Interpretable: it incorporates interpretable non-linear differential equations in tensor factorization so that it can reveal latent interactive relationships and thus generate future events effectively; (b) Dynamic: it can be aware of shifting trends by switching multi-aspect factors while summarizing their characteristics incrementally; and (c) Automatic: it finds every factor automatically without losing forecasting accuracy. Extensive experiments on real datasets demonstrate that our algorithm extracts interpretable interactions between data attributes, while simultaneously providing improved forecasting accuracy and a great reduction in computational time.