Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
Authors: Yiyue Qian
Yiming Zhang
Yanfang Ye
Chuxu Zhang
Venue: NeurIPS 2021 Poster (2021)
Keywords: graph representation learning
few-shot learning
drug trafficker detection

Abstract: Driven by the considerable profits, the crime of drug trafficking (a.k.a. illicit drug trading) has co-evolved with modern technologies, e.g., social media such as Instagram has become a popular platform for marketing and selling illicit drugs. The activities of online drug trafficking are nimble and resilient, which call for novel techniques to effectively detect, disrupt, and dismantle illicit drug trades. In this paper, we propose a holistic framework named MetaHG to automatically detect illicit drug traffickers on social media (i.e., Instagram), by tackling the following two new challenges: (1) different from existing works which merely focus on analyzing post content, MetaHG is capable of jointly modeling multi-modal content and relational structured information on social media for illicit drug trafficker detection; (2) in addition, through the proposed meta-learning technique, MetaHG addresses the issue of requiring sufficient data for model training. More specifically, in our proposed MetaHG, we first build a heterogeneous graph (HG) to comprehensively characterize the complex ecosystem of drug trafficking on social media.  Then, we employ a relation-based graph convolutional neural network to learn node (i.e., user) representations over the built HG, in which we introduce graph structure refinement to compensate the sparse connection among entities in the HG for more robust node representation learning. Afterwards, we propose a meta-learning algorithm for model optimization. A self-supervised module and a knowledge distillation module are further designed to exploit unlabeled data for improving the model. Extensive experiments based on the real-world data collected from Instagram demonstrate that the proposed MetaHG outperforms state-of-the-art methods.

Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
Yiyue Qian Yiming Zhang', Yanfang Yel.2-, Chuxu Zhang
Department of Computer and Data Sciences, Case Western Reserve University, USA 2 Department of Compute Science and Engineering, University of Notre Dame, USA DDpaarment Computer Science, Brandeis University, USA (yiyue.qian, wiimiig.maangg. yanfang-ye ye) @casee.du, chuxuzhang @ brandeis.edu
Abstract
Driven by the considerable profits, the crime of drug trafficking (a.k.a. illicit drug trading) has co-evolved with modern technologies, c.g., social media such as Instagram has become a popular platform for marketing and selling illicit drugs. The activities of online drug trafficking are nimble and resilient, which call for novel techniques effectively detect, disrupt, and dismantle illicit drug trades. In this paper, we propose a holistic framework named MetaHG to automatically detect illicit drug traffickers social media (i.e., Instagram), by tackling the following two new challenges: (1) different from existing works which merely focus on analyzing post content, MetaHG capable of jointly modeling multi- modal content and relational structured information on social media for illicit drug trafficker detection; (2) addition, through the proposed meta-learning technique, MetaHG addresses the issue of requiring sufficient data for model training. More specifically, in our proposed MetaHG, we first build heterogeneous graph (HG) comprehensively characterize the complex ecosystem of drug trafficking on social media. Then, we employ a relation- based graph convolutional neural network learn node (i.e., user) representations over the built HG, in which we introduce graph structure refinement to compensate the sparse connection among entities the HG for more robust node representation learning. Afterwards, we propose a meta-learning algorithm for model optimization. self-supervised module and a knowledge distillation module are further designed to exploit unlabeled data for improving the model. Extensive experiments based on the real-world data collected from Instagram demonstrate that the proposed MetaHG outperforms statc-of-the oftthe-rrttttttt-- methods.
Introduction
As the market illicit drugs (e.g., heroin, synthetic opioids such as Fentanyl) is considerably lucra- tive, the crime of drug trafficking (a.k.a. illicit drug trading) has never stopped but co-evolved with the advance of modern technologies, e.g., the practice of illicit drug trade has transformed from the physical world to online platforms. It has been shown that major social media platforms, including Instagram, Twitter, Snapchat, and Facebook, have become direct-to-consie consumemm marketing mediums for illicit drug traffickers 25). As illustrated in Figure I due the convenience and popularity, drug traffickers can easily create accounts on these platforms to advertise and sell drugs by posting code- words (e.g., street name) and images of drugs that are in stock (shown in Figure T (a)); they can also post their drug sale websites and utilize social media platforms for promotion (shown in Figure | (b)). Buyers can access illicit drugs and trade with vendors easily through these social media platforms.
"orrespoondigg authors.
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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Illegal drug trading has turn into a global concern due to its catastrophic consequences on society, from vi- olent crimes to public health (e.g., over 87, $70000 people died from drug overdose in 2020 in the U.S. and broke the record with 20% increase in a year [41)
profile
put+.30
ESer narme
descrigtion
Despite the persistent effort by law enforcement agen- cies, due to the considerable profits, activities of on- line drug trafficking are nimble and resilient: as shown in Figure (c), to avoid being banned, skilled drug traffickers (e.g,, with 1,002 followers, and 118 fol- lowings) rarely post drug description or images on social media, but instead, they advertise drugs im- plicitly using the slang (e.g., using "KET" to refer ketamine) and leave encrypted chat tool contact infor- mation (e.g., Kik Messenger) through their comments to other users' posts. To combat online drug trafficking, there is an imminent need to develop novel techniques for effective detection of drug traffickers on social media and thus enable law enforcement for proactive interventions to disrupt and dismantle illicit drug trades.
(b) External drug
sale website
post imtage do
Joh
contact
drug
Salle
weeks apo
(a) Drug trafflickig on Instagran am
(6) Detection challenge
Figure 1:
Showcase of drug trafficking on social media platform.
To tackle the above problem, there have been many research efforts on the investigation of online drug trafficking. Some existing works focus on analyzing the drug trafficking activities on darknet markets [46] 47 9 while some methods have been proposed for detecting illicit drug trading activities on social media [20 21 22]. Most of these works usually rely on a single type of content feature (e.g., text or image) while ignoring the structured relationship information among entities on social media. For instance, Instagram monitors drug traffickers simply by filtering drug-related keyword hashtags (e.g., weed4sale) [14. Unfortunately, as illustrated in Figure (c), drug traffickers may always invent new tactics to evade detection. Those methods that fail to consider multi-modal features (e.g.. both text and image) and the structural relationships among entities (e.g.. users and posts) could limit the effectiveness of detecting drug trafficking activities. Additionally, most recent works (22, 23 20 require sufficient labeled samples to train the model for the detection of drug trafficking. For example, Mackey et al. (23 collected almost 612,000 posts on Twitter and only found 1,778 drug trafficking-relater posts. Obtaining labeled samples social media is always expensive, and existing models may suffer from the constraint of few labeled data to detect drug traffickers.
To address the above challenges, in this paper, propose a holistic framework named MetaHG (as shown in Figure3) to automatically detect illicit drug traffickers on social media (i.e., using Instagram as a showcase). First, to overcome the shortcomings of merely analyzing single type of content information (e.g., text or image), we propose to consider the multi-modal content features (i.e., both text and image) and introduce a heterogeneous graph (HG) to model the relationships among three types of entities (i.c., user, post, and keyword). The constructed HG is able to comprchensively characterize the complex ecosystem of drug trafficking on social media. And then we exploit relation- based graph convolutional neural networks (R-GCNs) (32] to fuse both relational information among entities and content features to obtain initial node embeddings over the built HG. Due to spare relational connections among some entities, we further introduce graph structure refinement (GSR) [24 [16] to learn more robust node representations on the HG. Furthermore, address the few labeled data issue, we propose a meta-learning framework to transfer knowledge from training tasks and effectively adapt them testing tasks (e.g., new types of drug traffickers with few labeled samples). More specifically, to exploit unlabeled information for better performance, our proposed supersuue learning
model consists of two additional modules: an embedding similarity- module to augment R-GCNs for node representation refinement, and knowledge distillation module to facilitate better model optimization. In summary, the major contributions of our work include:
We study the illicit drug trafficker detection on social media and create a new dataset on Instagram (including text, image and relation information) for the problem, which is novel and urgent.
To solve the problem, we develop a holistic yet novel framework (i.e., MetaHG) to jointly model both structured relations and unstructured content information for illicit drug trafficker detection, accounting for the constraints of graph sparsity and limited labeled data for model training.
Comprehensive experiments on the real-world dataset demonstrate the outstanding performance of MetaHG by comparison with the ofate-of-arts methods. To the best of our knowledge, MetaHG is the first work that automatically detects drug traffickers social media using few labeled data.
2

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As drug overdose deaths have continued to ierrasse over the past decade across the country. our proposed technique will have a significant societal impact to help address this critical issue.
2
Related Work
In this section, we briefly introduce relevant studies in three aspects: drug trafficker detection, graph neural networks, and meta-learning.
Drug Trafticker Detection. Most existing works analyze drug traffickers on darknet markets 28 46
For instance, Zhang et al. [46] developed a system by leveraging user's styles to
detect drug traffickers on darknet markets. As drug trafficking appears on the surface web dramatically recently, some recent works [20 23] detect illicit drug traffickers on social media platforms by analyzing the single type of text information or image information of posts. For example, Li et al. [20] used a RNN model to learn the text pattern of posts to detect drug dealers on Instagram. However, these methods seldom consider multi-modal features and the relational information among entities. Different from existing works, we detect these illicit drug traffickers on social media by incorporating structural relation and unstructured multi-modal content information.
Graph Neural Networks. Most existing graph neural networks (GNNs) learn the node embedding by aggregating the features of neighborhood nodes 17 propagation rule to learn the node embedding, and GAT 39 proposes to learn different attention scores for neighbors when aggregating neighborhood information. However, most of these existing are proposed to deal with heterogencous graphs which more practical in reality. Inspired by GCN, Weighted- GCN [34] and R-GCNs [32] propose to learn node embedding based on multiple relational neighborhoods. Additionally, inspired by GAT, HAN 43 leverages the attention mechanism to learn the importance between nodes and meta-path simultaneously in heterogeneous graphs. Motivated by these studies, employ R-GCNs our base model to learn the node embedding in HG.
39
44 GCN [77 implements layer-wise
models work with homogeneous graphs. Some recent models [32 34
43 45)
Meta-Learning. Recent meta-learning models generally can be divided into two groups: metric- based meta-learning [4] 18, 28, 36 40) and gradient-based meta-learning [1] 8 10 [1
[9] 27]. For
metric-based models, they implement a generalized metric and matching functions from training tasks to train the model. For instance, Matching Networks 440 learns network that maps the labeled support set and an unlabelled example to the label in support set. Prototypical networks (36) learns metric space in which classification can be performed by computing distances to prototype representations of each class. For gradient- baaded models, they employ existing tasks data to learn well initialized model parameters that can be fast updated new tasks with few data [11] or directly
implement a meta-optimizer to learn the optimization process (38]. For example, Finn et al. [10] proposed MAML, in which the parameters of the model explicitly trained such that several gradient steps with few training data from new task will produce good generalization performance on that task. In this paper, motivated by MAML, we introduce a meta-learning framework to address small labeled data challenge of illicit drug traffickers detection.
3 Preliminary
3.1 Problem Definition
Given a social media dataset, we build a heterogeneous graph (HG) to comprehensively depict the rich information among the dataset for learning the user embedding. Let G = (V, X') denote HG, where V is the set of different types of nodes, E C V x V is the set of edges, and X' is the node attributes (features) set. Specifically, there are three types of nodes (user, post, and keyword) and eight types of relations (e.g., user-reply-pose any type of entities and an edge can be regarded as any type of relations between two nodes. The node attribute can be considered as the feature applied to the node. The goal is to learn the user embedding which can fed to a classifier for drug trafficker detection. Given the features of all user nodes X, (x1.... .NN) (N: number of users) and their binary labels Y = (V1, (..,..., yNN (y/ = denotes drug trafficker and yi = represents regular user), we target learning mapping function (detection function) Ug X, Y (with parameter Φ). Unlike existing works that use sufficient
in HG (see Figure 2 A node can be regarded as
3

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samples for model training, as the number of drug trafficker samples collected from social media is limited, we consider a more practical scenario that only few labeled data are available. Following the definition of few-shot learning [10), given the labeled data of existing different types of drug traffickers (e.g., stimulants trafficker (refer to the Supplementary Material 1.1)), we aim to build a classifier that can effectively detect drug traffickers of new types with few labeled data (e.g., opioid trafficker). Formally, the problem defined as follows.
Problem 1. Drug Trafficker Detection on Social Media. Given a social media data denoted as HG G and a set of different types of drug traffickers with their features X, = (I1,..., .IN and corresponding labels Y (y1.....yhh .YN) (training data), the problem is to build a machine learning model to detect illicit drug traffickers of new types that only have few labeled samples (testing data).
3.2 Relational Graph Convolutional Networks
Relational graph convolutional networks (R-GCNs) 32 has been proved be powerful for learning the node representation in HG. Different from GCN, R-GCNs considers the different types of relationships between two nodes when aggregating features from neighborhoods. Thus, we employ R-GCNs the base model to learn user representations in HG. According to the definition of R-GCNs, The layer-wise propagation rule can be formulated as follows:
M = a + YER JENT
Σ
Why + Woh)).
N
where n1t11 denotes the representation of node at + layer, W is learnable weight, NY denotes the set of neighbors of node i under relation r E RR and R is the set of relation type in HG. For simplicity. we use R-GCNs(X. X, A, R) denote a R-GCNs model where Z is the node embedding, X is the attribute feature matrix, and A is the adjacency matrix based on R.
4 Methodology
In this section, we present the details of our proposed model MetaHG (Figure (3). At first, we construct HG to depict both relation and multi-modal content information on social media, and introduce graph structure refinement (GSR) generate fined HG for compensating the spare connection among some en- tities. Then we utilize R-GCNs over the learned graph to learn node embedding and augment the model by an embedding sim- ilarity based self-supervised learning module (SSL). Lastly, we design a meta-learning algorithm for model optimization, which is further enhanced by knowledge distillation module (KD).
User
Raply
Post
Meution Hove
Inciade
Presist
Rution
Augger
Keyword
Figure 2: Network schema of HG for social media.
4.1 HG Construction on Social Media
To comprehensively describe illicit drug traffickers on social media, besides text and image content, we also consider the structural relation on social media. As shown in Figure 2 we build HG with three types of entities (user, post, and keyword) and eight types of relations as well as features of each node, such that both content and relation information can be exploited simultaneously. Next, we introduce the content feature and relation information in detail.
Table Content and relation information in HG for social media.
Content Feature
username, profile information.is business,
User
# of fullowersffollowins//oo
text content, image information comment content, # of likes/comments
Post
keywords extracted from all content
Keyworl
(e.g.. oxycodone, fentanyl, codine, and LSD)
Content Feature. Most users on social media post images and text simultaneously. Thus, we con- sider both text feature and image feature of posts and users. Text Feature: we first merge all of text in- formation as the corpus to fine-tune the pre-trained language model BERT [5] and convert all of the
Relation
RI: user- user-followe www.ffllloooollwwd-sseeeee R2: usgr-tagger
R3: user-reply
R4: user- mver-mention-pe
R5: user-have-post
R6: pre--prrlllekewwoon
R7: sest-include-kewwwrr
R8: post-tag-keywool
4

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Construction
(c) Selp-Supereeee R-GCNs
(d) Meta Knowledge Distillation
Ts.
meta-training
refined praph
relanion !
Papport
PrBy
Supaor
enery
F(D
(b) Graph Structure Refinement
Joncher
11
PiB)
stutennt
Juppory
original graph
Teatars Aisllarto
Heaianty par
classiiiation
Figure 3: The overall framework of MetaHG: (a) HG construction; (b) Graph structure refinement; (c) Self-supervised R-GCNs for generating node embedding; (d) Meta-learning with knowledge distillation for drug trafficker detection.
text information for each node to fixed-length feature vector (dimension 200). Particularly, for keyword nodes in HG, we first extract keywords from the corpus and then select a set of illicit- oriented keywords based word frequency. Furthermore, for each keyword node in HG, we feed the keyword to BERT to obtain the feature vector. Image Feature: we employ the pre- trained image model VGG19 335 acquire the image feature vector (dimension 1000) for each image and then implement PCA 33 to decrease the dimension from 1000 to 200. Note that the image feature for a user node is the image feature vector of the user profile image, and the image feature for post node the image feature vector of the post image. For all keyword nodes, the image feature is set as zero. Finally, both text and image features are concatenated as the attribute applied to each node.
Relation.
To determine whether a user is a drug trafficker on social media, we not only consider the content-based features (text and image features), but also the complex relationships among users, posts, and keywords. To characterize the relatedness of two nodes, we consider eight kinds of relationships (R1-R8 in Table 1 as follows: RI: user-folloofffollowwddss denotes that user is following or followed by another user; R2: user-tagger-use
-uegg user denotes that a user tags another user; R3: user-reply-pon denotes that user replies post; R4: user-mention-pon denotes that a user mentions another user in a post; R5: user-have-poss denotes that a post belongs to the user; R6: user-profile-keyword denotes that the profile description of a user contains the keyword; R7: post-inciude-keywonl denotes that the post content includes a certain keyword; R8: post-tag-keywon denotes that the content of a post has the hashtag keyword.
4.2 HG Structure Refinement via Metric Learning
As some users appear to be inactive on social media, the HG constructed in the previous step is sparse in connections among some nodes. That is, the original HG is not optimal for detecting drug traffickers on social media. To address this problem, we propose to generate a better HG by graph structure refinement with metric learning (GSR). Specifically, between node features to infer the potential connections between nodes by metric learning and obtain the feature similarity graph (FSG). Then, the original HG and FSG combined to obtain refined graph (see Figure 3 (b)). The generation process of FSG is described as below.
first calculate the similarities
and
Feature Similarity Graph. Since the dimension of different types of node features dif- ferent, we first adopt a type-specific mapping layer to project all of the node features X to a common feature space:
X'=o(X, Wt bz),
where a is the sigmoid activation function, X; is the feature vector of node vs We and by denote the projection matrix and the bias vector for node type l respectively. Afterwards, we implement metric learning on node features the common space and obtain a FSG G' where the edge between node pair and oj is obtained by:
E/15 - (
X(X,,X) 2
(3)
otherwise
5

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where e E [0, 1] is the threshold value controlling the density of the generated FSG. E(X; X,) is
parameterized function calculate the similarity between two nodes:
E(X), x,) a( @ X, Wa2 @ Xj),
(4)
where I denotes a similarity function (cosine similarity in this work), Wa1 and Wa2 are the param- eters which measure the importance of different feature dimensions, and o denotes the Hadamard product. The refined graph G is obtained by combining the original HG 9 and the generated FSG g'. In order make G' be relatively sparse and avoid overfitting for the downstream task, introduce structure sparsity regularizer via L1 norms:
Eger ||A'||,
(5)
where A is the adjacency matrix of g¹.
4.3 HG Representation Learning with Self-Supervised Augmentation
With the refined graph g' obtained in the previous step, we implement R-GCNs to learn node (i.e., user) embedding for drug trafficker detection. In particular, we stack R-GCNs layers to generate node embedding (Equation D For each user node E VS, we feed h/ into a fully-connected layer and softmax function to predict illegal value of drug trafficker, i.e., Y; = ha We. The objective is to jointly optimize the drug trafficker detection loss (i.e., the cross-entropy loss between the given drug trafficker labels and the predicted results Y) and the GSR loss Lysr:
C C= + toget Ager
Σ Y, log(Y:) + Ager ||
where 1o is the node set of labeled users in HG, Y is the user label set, and Agar is trade-off weight.
Self-Supervised Augmentation. Existing GNNs 17 12 26 30
utilize the local neighborhood information and feature content to generate the node embedding. However, the representation learning in most GNNs is mainly dominated by local neighborhood information [15). We wish to achieve a balance among the importance of local neighborhood and node features. Hence, introduce node embedding similarity based self-supervised module (SSL) using unlabeled data to enhance the HG representation learning (see Figure 3 (c)). Specifically, given a node u, we define
sets of nodes V and V having the M highest similarities and M lowest similarities with U; respectively. Based on the similarity (cosine similarity) among pairs of original node features, we can define node pairs in D as positive samples while node pairs in D as negative samples. We wish that the embedding of node pairs in V should be more similar while the embedding of node pairs in V should be less similar. Hence, in each training epoch, we concatenate the embedding of each node pair (v,, vy) A q) in V and D respectively denoted as Xiq. and assign each pair with label Yıg. Yú, = 1 when "y V otherwise Y,, 0 when By V Afterwards, we feed the node pair embedding X.g into a fully-connected neural network, i.e., Yig = Xig Weat, to obtain the predicted label. Finally, the final objective of node representation learning is to minimize the joint loss including drug trafficker detection loss, the SSL loss, and the GSR loss:
Cjoint = Ce+ Asst + - = Σ Y2 log((.) - Aust Σ
ΣEO Yig log((1g) (1g))++++00 + Ager || REV
VeVe
where 1/ 1 U U.N and Agar the hyper-parameters of SSL. By minimizing Ljoints parameters of R-GCNs, SSL, and GSR are optimized jointly for downstream drug trafficker classification task.
4.4 Model Optimization with Meta-Learning
We propose to leverage the meta-learning technique 10 for model optimization since the labeled data of drug traffickers is limited. In particular, it applies the gradient- gradient-based algorithm that learns well initialized model parameters (using training tasks data) which can be quickly adapted unseen new tasks. In this paper, given set of tasks T defined as drug trafficker detection problem (binary classification) of different trafficker types, we choose three drug trafficker types with a relatively large number of samples (c.g., stimulants trafficker) meta-traine meta-training tasks and the other two types with few

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Algorithm Training Procedure of MetaHG
Require X, A, R, Φ: nodes features, adjacent matrix, node relations, and initial parameters.
Learn a refined graph via Equation 3 and A
Implement self-supervised learning to augment R-GCNs on the refined graph. while not convergae do
Sample a batch of meta-training tasks 7 from T. for each do
Sample support set S- and query set Q.. Update the parameters of via Equation 8 end for
Update the parameters & of task-agnostic model via Equation 9
Sample support set S, and query set 2, from meta-testing tasks. Distill the soft knowledge from teacher model using the unlabeled data Q, via Equation I0 Update the parameters of student model via optimizing Equation end while
Return Optimized o
samples (e.g., opioid trafficker) as meta-testing tasks. Then, we divide the data of each task T E T into support set S, and query set Q.. Afterwards, the classifier first updated to task-specifie model on meta- meta-training tasks with support set S,, and is further optimized to task-agnostic model using Qr of training tasks, which is called meta-training. After sufficient training. the learned model can further adapt to new testing tasks with few data samples in support set, which is called meta-testing. Let be the set model parameters. For a certain classification task т. we begin with feeding S, to the model and calculate the loss C, on 2, to update & to o, through gradient descent:
of = ó - a V (o),
where a is the step size for inner-level meta-training. Later, the model (with parameter Φ) is further updated in task-agnostic manner:
-- Vo (o) TET
where 33 is the learning rate. After sufficient training over meta meta-training training tasks, the well initialized parameters is further adapted for prediction over meta-testing tasks.
Meta Knowledge Distillation for Model Adaptation. The above optimization strategy di- rectly adapts the classifier learned from the labeled data meta-training to new testing tasks, while ignoring the the hidden information of unlabeled data during model training. Inspired by the knowledge distillation technique [13]. we develop a meta knowledge distillation module (KD) that utilizes the unlabeled data in meta-learning transfer soft knowledge from the meta-training classifier (teacher model) to the meta-testing model (student model) (Figure (d)). Mimicking teacher's prediction results enables student model learn the secondary information that cannot be
expressed by the labeled samples in meta-testing data alone. Soft knowledge from teacher model is formulated the predicted probability of drug traffickers in meta-testing data:
exp (Z./tt]
pf (Z.,t) Softmax (((,)) Σ exp (Za(t))
(10)
where Z, is the embedding of node v in 2, for meta-testing task т, f fZ,) the score logit that Z, achieves on label 1, and is the temperature index to soften the peaky softmax distribution [13 Thus, the knowledge distillation loss of teacher model and student model defined as follows:
Led = -8 Σ
PT (Z. (P. (SS t)),
(1I)
WER C=0
where PT and PS are the predicted distributions of teacher model and student model respectively. Afterwards, we combine the KD loss Lid on unlabeled data (query data) and the cross-entropy loss
fee on labeled data (support set):
Liotal Loe AeaCod,
(12)
7

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where And is the trade-off weight for balancing the two losses. Taking the model (with parameter の) obtained by Equation 9 as the initial model, the student model for meta-testing tasks are further updated (fine-tuned) as follows:
a V (total()).
(13)
where a is the learning rate. Finally, the fine-tuned student model is used to detect drug trafficker in query set of meta-testing tasks. The pseudo-code of MetaHG training procedure is shown in Alogrithm
5 Experiments
In this section, we first create novel dataset for the problem. Then we conduct extensive experiments to evaluate the performance of our model. Further analysis is provided to show the effectiveness of each model component,
sensitivity, and model stability.
5.1 Novel Dataset for Drug Trafficker Detection
Existing works of drug trafficker detection on social media merely rely text or image data and most of the datasets are not publicly accessible. Thus, we create a new dataset crawled from Instagram (a very popular social media platform worldwide [22) for the problem. Specifically, we first utilize set of illegal drug related keywords (e.g., weed, LSD, and fentanyl) to crawl the public user profiles and the corresponding posts using the public official Instagram API 7 from Jan 2020 to Jan 2021. Note that all collected data are public and we have anonymized the personal information of public users in our dataset. To obtain the groundtruth labels, we (including 3 groups, 2 annotators in each group) spent two months collecting and manually labeling these users into six groups (regular user, stimulants trafficker, hallucinogens trafficker, opioids trafficker, hidden trafficker, and mixture trafficker) according to the criterion in [33], In total, we obtain 8,651 users (including 3,242 drug traffickers and 5,409 regular users) and 79,705 posts. Based the dataset, we construct HG which has 129,894 nodes (including 8,651 user nodes, 79,705 post nodes, and 41,538 keyword nodes) and 218,039 edges of eight relations (Table T. Details of this dataset is provided in Supplementary Material 1.1.
5.2 Baseline Methods
We compare our model with twenty baseline methods spanning several categories:
Traditional Classification Methods. We take text features (tFeature), image features (iFeature), and the combination of them (cFeature) the feature vector for each user respectively, and feed them to generic 4-layer deep neural network (37 (DNN) (BI). We also consider two existing using tFeature for drug traffickers detection (B2).
works 20 22
Meta-Learning Models. For meta-learning baseline methods, besides MAML 10 also employ two popular few-shot learning models, i.e., Matching Network (MatchingNet) 40 and Prototypical Network (ProtoNet) 36 train meta-learner with cFeature (B3).
Graph Representation Learning Models. We take eFeature as node attribute information and apply six popular graph representation learning models to learn user embeddings in HG, i.c.,
Deepwalk (29]. metapath2vec 66) GCN [17], GAT [39, HAN 4X), and R-GCNs [32]. The learned user embeddings are further fed to a 4-yayer DNN (B4) for classification. In addition, we also apply MAML. to train these graph representation learning models (B5).
The details of all models are discussed in Supplementary Material 2.
5.3 Performance Comparison
reportss the performances of all models. The best performances are highlighted bold. All baseline models are divided into five groups (B 1B5) and the shot number denotes the support data size model training and testing. According to the table, we can conclude that (i) The combination of text and image features (cFeature) contributes larger than single text image features for drug traffickers detection (B1). shows that both text and image are very indispensable to describe users on social media. (ii) By comparing results in B1 and B4, we find that considering the relationships among
8

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Table 2: Performance comparisons of all methods with different support data sizes (shot numbers).
Setting
1-shot
5-shot
10-shot
20-shot
Group
Model
ACC
F1
ACC
F1
ACC
F1
ACC
F1
+Feature+DNN
37
0,4786
0.3768
0.5046
0.3959
0.5215
0.4116
0.5420
0.4230
B1
iFeatureDDNN
0.5253
0.4256
0.5428
0.4432
0,5564
0.4635
0.5712
0.4841
CFeature+DNN
0.5468
0.4434
0.5601
0.4553
0.5758
0.4755
0.5935
0.4924
Li et el.
20
0.4929
0.3942
0.5287
0.4171
0.5496
0,4280
0.5701
0.4456
B2
Rokon al.
22
0.5058
0.4037
0.5396
0.4260
0.5585
0.4376
0.5765
0.4529
Preature+Protooet
36
0,5815
0.5735
0.6156
0.5987
0,6321
0.6293
0.6637
0.6572
B3
Fecature+MatcchnngNe
40
0.6058
0.5953
0.6337
0.6257
0.6551
0.6478
0.6773
0.6659
cFeature+MAML
10
0.6337
0.6218
0.6654
0.6525
0.6872
0.6735
0.6959
0.6985
29
DNN
0.5919
0.4991
0,6210
0.5251
0.6472
0.5449
0,6710
0.5605
6
metapath2vec+DDNN
0.6257
0.5243
0,6518
0,5549
0.6739
0.5683
0.6953
0.5825
B4
17
GCN+DNN
0,6524
0,5510
0,6853
0.5772
0.7054
0.5953
0.7291
0.6138
39
GAT+DNN
0,6650
0.5558
0.6889
0.5842
0.7129
0.6023
0.7305
0.6195
43
HAN+DNN
0.6786
0.5629
0.7047
0.5925
0.7207
0.6152
0.7421
0.6290
82
R-GCN++DNN
0.6836
0,5765
0.7183
0.6042
0.7254
0.6221
0.7476
0.6446
Deepwalk+MAML
0.6962
0.6957
0.7324
0.7306
0.7559
0.7537
0.7751
0.7674
metapath2ve+-AMML
0.7251
0.7232
0.7622
0.7534
0.7837
0.7746
0.7995
0.7921
B5
GCN+MAMI
0.7526
0.7407
0.7835
0.7827
0.8052
0.7924
0.8319
018356
GAT+MAML
0.7578
0.7426
0.7905
0.7921
0.8124
0.8036
0.8439
0.8214
HAN+MAML
0.7732
0.7654
0.8062
0.7959
0.8328
0.8176
0.8551
0.8327
R-GCNs+MAML
0.7853
0.7727
0.8253
0.8149
0.8465
0.8352
0.8678
0.8535
Ours
MetaHG
0.8489
0.8480
0.8873
0.8758
0.9196
0.9122
0.9354
0.9311
entities can help improve the model performance. (iii) The performance of meta-learning models with combined features (B3) or node embeddings (B5) are much better than traditional classification models (B1 and B2), showing the effectiveness of meta-learning for solving this problem. (iv) In all
cases, MetaHG significantly outperforms all baseline methods, demonstrating the strongest capability of our model for drug traffickers detection on social media.
5.4 Ablation Studies
Table 3: Results of different model variants.
Our model MetaHG integrates four crucial components, i.e., graph structure refinement (GSR), self-supervised learning in R-GCNs (SSL), meta-learning (MAML), and knowledge distillation (KD). To verify the effectiveness of
1-shot
5-shot
20-shot
Model
ACC
F1
ACC
F1
ACC
Fl
MetaHG
0.8489 0.8480
0.848) 0.8873 0.8758/0.9354 0.9311
MAML (AI) 0.7247 0.6125 0. 6.57770 0.6851
each component, we conduct ablation studies
KD (A2)
0.7914 0.7922 0.8356 0.8247 0.8804 0. 0.8689
GSR (A3)
0.8078 0.8018 0.8457 0.8351 0.8934 0.8857
by removing each of them independently. The
SSL (A4)
0.8147 0.8052 0.8473 0.9056 0.9005
results of different model variants are reported
in 3 We first replace MAMI. with a 4-layer DNN (A1), which means we implement the node
embedding generated from SSL augmented R-GCNs over the refined graph. Similar to the data setting in B1 and B4, all labeled data in meta-training tasks and few-shot labeled data (support set) in meta-testing tasks are for model training. and the rest of data in meta-testing tasks (query set) are for model evaluation. We conclude that MAML has the greatest contribution to MetaHG. Then we remove KD on MAML (A2) and find the performance decrease obviously, showing the effectiveness of KD. Furthermore, performances of A3 and A4 drop almost 4% and 3% respectively compared with MetaHG, validating that both GSR and SSL are effective to enhance the model performance for the detection problem.
remove GSR (A3) and SSL (A4) from MetaHG respectively and find the
5.5 Hlyper-paramete Sensitivity and Model Stability
(a) graph structure refinement
(b) seff-suparvised learning
To explore the hyper-parameter sensitivity, we conduct three analysis experiments w.r.t. on
0.500
5505
2875
GSR, Aeat on SSL, and Akd on meta-learning. Specifically, in Figure (a), we vary in Eq. 3 to control the density of the generated graph in GSR. We find that the model performance in- creases with the increment of and the optimal value is 0.95, while the performance decreases when e goes beyond the optimal value because the graph will be too sparse with a larger simi-
0.25
2825
0.72
30
8.775
knowledge distillation
(d) model stabilit analysis
1.95
0.56
0.30
0.59
5378
E750
Figure 4: model performance over different testing tasks (d).
sensitivity (a)-(c) and

---

larity threshold. Additionally, in Figure (b), we vary the value of Aas! in Eq. 7 By comparing with Ass! = 0 (without SSL) and 2 5, we can further validate the effectiveness SSL in enhancing the model performance. The optimal value of Aas! is 5 and the performance drops with larger Aast due to the overfitting on the SSL. task. Moreover, in F we vary the value of Akd Eq. 12 and the optimal value Agd is 0.01 for our model, indicating that it is necessary to incorporate appropriate distilled knowledge. As shown Fig. 4 (d), we also analyze the stability of our model by comparing with three baseline methods on different testing tasks (TI: opioids trafficker detection, T2: hidden trafficker detection). MetaHG is obviously more stable and also better than baseline methods on both Tl and T2, showing the robustmess and effectiveness of MetaHG.
5.6 Embedding Visualization
(a) dDNnuneLDNN
(b) R.GONEIDNN
To examine the effectiveness of our model intuitively, we vi- sualize embeddings drug traffickers generated by cFeature + DNN, R-GCNs + DNN, R-GCNs MAML, and MetaHG respectively in Figure 5 The blue points represent the em- beddings for opioids traffickers while grey points show the embeddings for regular users on Instagram. We can see that MetaHG generates the most distinct boundaries and the small- est overlapping area between opioids traffickers and regular users, demonstrating the superiority of our model.
(o) R-GCN--MAMI.
(0) MesHG
Figure Embedding visualizations.
5.7 Case Study
We further analyze drug traffickers detected by MetaHG. Fig- ure fillustrates the superiority of our model by comparing with baseline models for real cases. MetaHG is able to detect hidden drug traffickers on social media although they pretend be regular users innocent users who seldom post any drug related information to the homepage. In this figure, drug trafficker "se**# 1" pretends be an innocent user who does not have any illicit information on his home- page. However, he advertises at least four types of drugs (i.e., LSD, MDMA, DMT, and Shrooms) by leaving the contact information of encrypted chat tools (i.e., WICKR, Kik, and Snapchat) to other user's 's posts. These hidden drug traffickers cannot be detected by existing models, while MetaHG can detect these hidden drug traffickers based on the relationships among entities on social media.
se*
uSET name
125 Followers Folowing
sefe#11
Glob Place ur
1/740-241-
contact in
WICKR// KIK//SNAPCHAT
GIETMA
MDMA, SHROOMS PILLS feel the spirit...
Fchj
drug sale
60m
Reply # weeks ago
Figure 6: A hidden drug trafficker.
Conclusion
In this paper, to solve the problem of illicit drug trafficker detection with limited labeled data constraints, we create new dataset and develop a novel system called MetaHG. Specifically, we first construct a HG to describe the structural relation and multi-modal content information in the dataset, and employ graph structure refinement to learn refined HG. Then we employ R-GCNs with self-supervised module to learn the node embedding in HG. Afterwards, we design meta-learning algorithm to optimize the model. A knowledge distillation module is further introduced to improve model optimization. The extensive results demonstrate the effectiveness of our model by comparison with many baseline models.
Acknowledgment
This work is partially supported by the NSF under grants IIS-2107172, IIS-2140785, CNS-19408599 I1S-2027127, IIS-2040144, IIS- 195-1951504 and OAC-1940855, the NIJ 2018-75-CX-
0032.
10

---

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13


Reviews:
[{"review_id": "qVYLqufsqL4", "reviewer": "Reviewer_jGcf", "summary": "This paper propose a novel framework MetaHG based on R-GCN for illicit drug trafficker detection on social media. A series of methods including meta learning(MAML), self-supervised learning(AS-SSL) and knowledge distillation(KD) are used to MetaHG, and obtain better metrics than traditional models with few labeled samples. In addition, the paper also creates a new dataset from Instagram for the task.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "This paper combines a series of methods including R-GCN, meta learning, self-supervised learning and knowledge distillation for illicit drug trafficker detection, and obtain better metrics than traditional models. A new dataset is also created for this task, which is a valuable contribution. Paper writing is overall very clear and persuasive with adequate experiments and high performance.\n\nSome questions for the author:\n1. The graph model seems to be undirected, but social network relationships are obviously directed.\n2. In Figure 4(d), what’s the definition of stability? How does it calculate and reflect stability?\n3. In case study, the homepage of “se***11” writes out several drug names straightforwardly. Why can’t existing models detect it? What improvements help MetaHG detect it?\n\nSome small points and feedback:\n1. In Figure 2(a), the word “vendor” is not mentioned which should be “user”.\n2. I think that creating a new dataset is valuable, the authors would consider mentioning it in the abstract.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "None.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "c82pM5b8gif", "reviewer": "Reviewer_VABt", "summary": "Summary: \nThe authors propose a model to detect drug-trafficker profiles on Instagram. The model consists of many steps and phases, but in short: they create a heterogeneous graph from users, keywords, and posts, and in addition to the regular cross-entropy loss they propose to add multiple regularizer terms extracted from several stages of their algorithm (i.e., a term to build the graph, a term to learn the node representations, and a term to distill the knowledge from an out-of-domain model).\n\nTo evaluate their model, the authors created a dataset and compared their model with many baselines and their combinations. They also reported several experiments, including user visualization and a case study.", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "Reasons to accept:\n\n- The model works very well in the dataset. The improvement over the baselines is substantial.\n\n- The authors release their code (not the dataset).\n\n\nReasons to reject:\n\n- The model is enormous, it has many moving parts. This makes the model generalization to other datasets and scenarios very questionable. The sheer size of the model is not the main reason to reject, however this property cannot be evaluated with only one dataset (as the authors did). The astronomical performance of the model might be due to particular properties of this dataset—this is very likely, because the model is employing many aspects and areas.\n\n- The model has many hyper-paremeters—I think five. None of them are discussed how they are tuned. This combined with the complexity of the model severely distorts the conclusions made in the paper. The last but not least is that the evaluations are made in the low-resource data regime. Practically such a gigantic model with many hyper-parameters is not tunable in semi-supervised settings.\n\n- Even though the model has several steps, almost all of the employed techniques pre-exist. The actual contribution is very limited.\n\n- Other minor issues:\n\n  - The methodology section is not written well, it has several grammatical issues.\n\n  - It is not described that where the numbers reported in Table 5 come from, what they represent, etc—in this table the main results are reported.\n\n  - In Equation 4, are not X_i and X_j projected into the same space? Why do they have different weight vectors W_s1 and W_s2?\n\n  - In Table 3, the ablation study shows that the meta-learning step is the most influential technique in the model. It is so effective that if we drop it from the model, the proposed model turns into a mediocre model, at best. Why is that? Do the baselines that employ meta-learning have the same module? This is not discussed in the paper.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "They have addressed it.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "LaqW0LHVs3k", "reviewer": "Reviewer_Ykxj", "summary": "The paper proposes a novel model MetaHG to automatically detect illicit drug traffickers on social media. It firstly builds a heterogeneous graph based on post content and relational structure information on social media to characterize the drug trafficking system. Then it integrates graph structure learning and relational-based GCN to learn robust node embedding on HG. Afterwards, it leverages meta-learning and knowledge distillation to optimize model parameters and further to detect drug traffickers on social media. Finally, it conducts extensive experiments to validate the effectiveness of the framework.", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "It is very interesting and meaningful to develop a novel model for drug trafficking detection on social media. I list the main strengths and weaknesses of the paper below.\n\nStrengths:\n\n+ This paper is well organized and easy to follow. It elaborates on background, related work, methodology, and experiments. \n\n+ To address the few labeled data issue, the proposed framework leverages meta-learning and knowledge distillation to optimize the model. In addition, to solve the problem of graph sparsity, it combines graph structure learning and relational GCN augmented by self-supervised learning to learn the robust node embedding on heterogeneous graph. The design of MetaHG framework is novel the technique quality of this paper is solid.\n\n+ The paper conducts comprehensive experiments to validate the effectiveness of the framework. It compares with five groups of baseline models, especially previous methods of the same topic. To show the effectiveness and robustness of the framework, it conducts detailed ablation studies for analyzing the effectiveness of each module and it does comparison experiments on different tasks for analyzing the model robustness.\n\nWeaknesses:\n\n- The paper doesn’t explain why the heterogeneous graph is sparse. It is common that the relationships among users and posts on social media platform (e.g., Instagram) can be dense. Why the heterogeneous graph in this paper is sparse?\n\n- The description of baseline models and their settings is short and some parts are confused. In Table 2 Group B2, it shows the results of other two papers with different shots, which is confused. The paper should introduce these two methods briefly.\n\n- Minor issue: In Figure 1 and Figure 5, I think “63w” and “60w” should be how many weeks ago did the comment post instead of the number of like. Please double-check it.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "This paper discusses the limitations and potential impact in the introduction and the supplement materials.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3h"}, "extra_text": {}}, {"review_id": "CHp_dqSJzG9", "reviewer": "Reviewer_77r1", "summary": "This work studied the task of detecting online drug trafficking using social media data. In particular, the authors proposed to use graph convolutional networks to model various types of entities in social media and the relations between them. Also, a meta-learning algorithm is used for the model optimization.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "This work presented an interesting idea of using the GCN-based method to model the social media network for online drug trafficking detection. The detailed comments are listed below: \n\n1. The novelty of the proposed model is relatively limited. Meta-HG is mostly applying existing techniques, e.g., R-GCN and meta-learning, to the application of drug trafficking detection. The major contribution may come from the combination of FSG and original HG, and AS-SSL. \n\n2. HG construction is not clearly described. First, some details of the text feature vector generation are missing. For example, how is the username embedded in BERT? The authors said they concatenate the extracted features. However, if that is the case, then embedding of post/user may have different dimensions since the post length, and user information varies. How is this situation handled by the proposed graph convolutional network? Second, it is not clear how the authors use the image feature to represent post/user. One post may contain multiple images, and one user may post multiple posts.  How is this situation handled during the HG construction?\n\n3. The proposed method models every entity in the social network in the graph, which raises the concern of the time/space complexity of the proposed model. It seems not practical to do this in a real-world situation, especially when the model tried to learn HG and FSG at the same time. \n\n4. How the HG and FSG are combined? By unioning the edge set of HG and FSG? \n\n5. The description of AS-SSL needs to be more clear about the terms such as node features, node attribute features, node attributes, node embeddings, node representations, and node pair features. \n\n6. What are the \"node features\" mentioned in line 228? Are they node features obtained in the HG construction? If this is the case, then it does not make sense to use node attribute features (which I assume is obtained in the HG construction) again for the classification task because this will make the AS-SSL part decoupled from the computation graph. If node features refer to the node embeddings learned generated while training on HG, it is better to highlight that the cosine similarity calculation is still a network layer, though no parameters are learned. Just to make sure that readers understand that the AS-SLL is still in the computation graph.  \n\n7. Is AS-SSL applied only on the user nodes because, in Figure 2c, only the \"vendor\" icon is used there. If so, the authors should state it clearly in the paper. \n\n8. The dataset collected is kind of \"ideal\" for the task because 37% of the users are drug traffickers. In practice, when the users want to increase the \"recall\" of detection of drug traffickers detection, they will increase the list of keywords which will inevitably bring \"noise\" into the graph. So reporting the model's performance in a less ideal setting would be interesting.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The limitations and potential negative societal impact of this work are addressed. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "8GlmoOokVkw", "reviewer": "Reviewer_igwA", "summary": "This paper presents a novel heterogeneous graph learning model for automatically detecting illicit drug trafﬁckers on Instagram. The proposed model addresses two challenges: 1) sparse graph structure, and 2) limited labeled samples for model training. The authors also collected a large social media dataset from Instagram and conducted a lot of experiments to evaluate the proposed model.", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "Overall, this work provides a complete study with sufficient motivation, novel techniques, and thorough experiments. More speciﬁcally, the authors consider the constraints of graph sparsity and limited labeled data for model design, which includes the three-fold contribution: 1) a novel framework named MetaHG to integrate both structural relations and unstructured content information for illicit drug trafﬁcker detection. 2) graph structure learning is used to refine HG structure and self-supervised R-GCN is employed to learn the node embeddings. 3) a meta-learning approach with knowledge distillation is adopted to optimize the model which could overcome the issue of insufficient labeled samples. I think it is an interesting work and has the ability for generalizing to other applications. \n\nOn the other hand, the design of the method is reasonable and comprehensive. Two challenges of sparse graph connection and insufficient labeled samples are well addressed. Moreover, the presentation is good with straightforward and clear writing. \n\nThere are some points where improvements could help further strengthen the paper:\nIn the methodology section, the motivation of modeling HG is not very clear. Why the heterogeneous nodes and relations can be useful for this task?\n\nThe paper uses eight types of relations to describe the relationships among different types of nodes. The authors should explain how to select these relations. Do they all benefit from this task?\n\nThe proposed self-supervised augmentation model is mainly based on the content information. I'm curious about the performance if it leverages some relations to implement the same model.\n\nDo the authors evaluate the training and testing time for the proposed model?\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "1.5 hours"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The reviewers generally agree that the paper is interesting and like the new application with a novel dataset that will be published, thorough experiments that establish a baseline, and a clear presentation. The reviewers provided recommendations that should clearly be incorporated in the paper.

Decision: Accept (Poster)