Hypergraphs, in contrast to general graphs, utilize hyperedges to connect multiple nodes, thereby inherently facilitating the representation of higher-order relational structures. To leverage the benefits of hypergraphs, several Hypergraph Neural Networks (HyGNNs) have been proposed to model hypergraph structures. Although existing HyGNNs excel at capturing complex relationships in homophilic hypergraphs, they still face challenges in modeling heterophilic hypergraphs, as most existing HyGNNs are designed based on the homophily principle. Recent studies have attempted to leverage attention mechanisms that are less reliant on the homophily principle. However, these attention mechanisms remain ineffective for nodes in heterophilic hypergraphs. To tackle the aforementioned challenges, we propose a novel Broadcast HyperGraph Neural Network (BHyGNN) to adaptively broadcast node information to learn more effective node representations in heterophilic hypergraphs. Specifically, we devise a novel Variational Broadcast Autoencoder Network to sample the broadcast and receive actions to propagate information between nodes and hyperedges. Moreover, we design an incorporation transformer mechanism to perform the estimated broadcast or receive actions to learn the hyperedge or node representations, incorporating the information from both sides. Extensive experiments over five benchmark heterophilic hypergraph datasets and six homophilic hypergraph datasets demonstrate the effectiveness of BHyGNN over all baseline methods. Our source code and datasets are available at https://github.com/Tianyi-Billy-Ma/BHyGNN.

The exponential growth of data-driven systems and AI technologies has intensified the demand for high-quality web-sourced datasets. While existing datasets have proven valuable, conventional web data collection approaches face significant limitations in terms of human effort and scalability. Current data collecting solutions fall into two categories: wrapper-based methods that struggle with adaptability and reproducibility, and large language model (LLM)-based approaches that incur substantial computational and financial costs. To address these challenges, we propose AutoData, a novel multi-agent system for Automated web Data collection, that requires minimal human intervention, i.e., only necessitating a natural language instruction specifying the desired dataset. In addition, AutoData is designed for a robust multi-agent architecture, featuring a novel oriented message hypergraph coordinated by a central task manager, to efficiently organize agents across research and development squads. Besides, we introduce a novel hypergraph cache system to advance the multi-agent collaboration process that enables efficient automated data collection and mitigates the token cost issues prevalent in existing LLM-based systems. Moreover, we introduce Instruct2DS, a new benchmark dataset supporting live data collection from web sources across three domains: academic, finance, and sports. Comprehensive evaluations over Instruct2DS and three existing benchmark datasets demonstrate AutoData's superior performance compared to baseline methods. Case studies on challenging tasks such as picture book collection and paper extraction from surveys further validate its applicability.

Among the existing self-supervised learning (SSL) methods for graphs, graph contrastive learning (GCL) frameworks usually automatically generate supervision by transforming the same graph into different views through graph augmentation operations. The computation-efficient augmentation techniques enable the prevalent usage of GCL to alleviate the supervision shortage issue. Despite the remarkable performance of those GCL methods, the InfoMax principle used to guide the optimization of GCL has been proven to be insufficient to avoid redundant information without losing important features. In light of this, we introduce the Graph Contrastive Learning with Cross-View Reconstruction (GCVR), aiming to learn robust and sufficient representation from graph data. Specifically, GCVR introduces a cross-view reconstruction mechanism based on conventional graph contrastive learning to elicit those essential features from raw graphs. Besides, we introduce an extra adversarial view perturbed from the original view in the contrastive loss to pursue the intactness of the graph semantics and strengthen the representation robustness. We empirically demonstrate that our proposed model outperforms the state-of-the-art baselines on graph classification tasks over multiple benchmark datasets.

In recent decades, due to the lucrative profits, the crime of drug trafficking has evolved with modern technologies. Social media, as one of the popular online platforms, have become direct-to-consumer intermediaries for illicit drug trafficking communities to promote and trade drugs. These group-wise drug trafficking activities pose significant challenges to public health and safety, requiring urgent measures to address this issue. However, existing works against the imminent problem still face limitations, such as primarily analyzing individual roles from a single perspective, ignoring the group-wise relationships, and requiring sufficient labeled samples for model training. To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities. Firstly, we build a hypergraph called Twitter-HyDrug including online user nodes and four types of hyperedges to depict the rich group-wise relationships among these users. Then, we leverage hypergraph neural networks to model the rich relationships among nodes and hyperedges in the drug trafficking hypergraph. Furthermore, we design a hypergraph self-supervised contrast module, which integrates the augmentation from the structure view and the attribute view to enhance hypergraph representation learning over unlabeled data. Finally, we design an end-to-end framework that combines the self-supervised contrastive module and the supervised module to classify online drug trafficking communities. To comprehensively study the online drug trafficking problem and evaluate our model, we conduct extensive experiments over Twitter-HyDrug and three citation benchmark hypergraph datasets to demonstrate the effectiveness of our model. Our new data and source code are available at https://github.com/HyGCL-DC.

GitHub, as the largest social coding platform, has attracted an increasing number of cybercriminals to disseminate malware by posting malicious code repositories. To address the imminent problem, some tools were developed to detect malicious repositories based on the code content. However, most of them ignore the rich relational information among repositories and usually require abundant labeled data to train the model. To this end, one effective way is to exploit unlabeled data to pre-train a model which considers both structural relation and code content of repositories, and further transfer the pre-trained model to the downstream tasks with labeled repository data. In this paper, we propose a novel model adversarial contrastive learning on heterogeneous graph (CLA-HG) to detect malicious repository in GitHub. First of all, CLA-HG builds a heterogeneous graph (HG) to comprehensively model repository data. Afterwards, to exploit unlabeled information in HG, CLA-HG introduces a dual-stream graph contrastive learning mechanism that distinguishes both adversarial subgraph pairs and standard subgraph pairs to pre-train graph neural networks using unlabeled data. Finally, the pre-trained model is fine-tuned to the downstream malicious repository detection task enhanced by a knowledge distillation (KD) module. Extensive experiments on two collected datasets from GitHub demonstrate the effectiveness of CLA-HG in comparison with state-of-the-art methods and popular commercial anti-malware products.

Driven by the exponential increase of software and the advent of the pull-based development system Git, a large amount of open-source software has emerged on various social coding platforms. GitHub, as the largest platform, not only attracts developers and researchers to contribute legitimate software and research-related source code but has also become a popular platform for an increasing number of cybercriminals to perform continuous cyberattacks. Hence, some tools have been developed to learn representations of repositories on GitHub for various related applications (e.g., malicious repository detection) recently. However, most of them merely focus on code content while ignoring the rich relational data among repositories. In addition, they usually require a mass of resources to obtain sufficient labeled data for model training while ignoring the usefully handy unlabeled data. To this end, we propose a novel model Rep2Vec which integrates the code content, the structural relations, and the unlabeled data to learn the repository representations. First, to comprehensively model the repository data, we build a repository heterogeneous graph (Rep-HG) which is encoded by a graph neural network. Afterwards, to fully exploit unlabeled data in Rep-HG, we introduce adversarial attacks to generate more challenging contrastive pairs for the contrastive learning module to train the encoder in node view and meta-path view simultaneously. To alleviate the workload of the encoder against attacks, we further design a dual-stream contrastive learning module that integrates contrastive learning on adversarial graph and original graph together. Finally, the pre-trained encoder is fine-tuned to the downstream task, and further enhanced by a knowledge distillation module. Extensive experiments on the collected dataset from GitHub demonstrate the effectiveness of Rep2Vec in comparison with state-of-the-art methods for multiple repository tasks.

Opioids (e.g., oxycodone and morphine) are highly addictive prescription (aka Rx) drugs which can be easily overprescribed and lead to opioid overdose. Recently, the opioid epidemic is increasingly serious across the US as its related deaths have risen at alarming rates. To combat the deadly opioid epidemic, a state-run prescription drug monitoring program (PDMP) has been established to alleviate the drug over-prescribing problem in the US. Although PDMP provides a detailed prescription history related to opioids, it is still not enough to prevent opioid overdose because it cannot predict over-prescribing risk. In addition, existing machine learning-based methods mainly focus on drug doses while ignoring other prescribing patterns behind patients' historical records, thus resulting in suboptimal performance. To this end, we propose a novel model DDHGNN - Disentangled Dynamic Heterogeneous Graph Neural Network, for over-prescribing prediction. Specifically, we abstract the PDMP data into a dynamic heterogeneous graph which comprehensively depicts the prescribing and dispensing (P&D) relationships. Then, we design a dynamic heterogeneous graph neural network to learn patients' representations. Furthermore, we devise an adversarial disentangler to learn a disentangled representation which is particularly related to the prescribing patterns. Extensive experiments on a 1-year anonymous PDMP data demonstrate that DDHGNN outperforms state-of-the-art methods, revealing its promising future in preventing opioid overdose.

Graph contrastive learning (GCL), leveraging graph augmentations to convert graphs into different views and further train graph neural networks (GNNs), has achieved considerable success on graph benchmark datasets. Yet, there are still some gaps in directly applying existing GCL methods to real-world data. First, handcrafted graph augmentations require trials and errors, but still can not yield consistent performance on multiple tasks. Second, most real-world graph data present class-imbalanced distribution but existing GCL methods are not immune to data imbalance. Therefore, this work proposes to explicitly tackle these challenges, via a principled framework called \textit{\textbf{C}o-\textbf{M}odality \textbf{G}raph \textbf{C}ontrastive \textbf{L}earning} (\textbf{CM-GCL}) to automatically generate contrastive pairs and further learn balanced representation over unlabeled data. Specifically, we design inter-modality GCL to automatically generate contrastive pairs (e.g., node-text) based on rich node content. Inspired by the fact that minority samples can be ``forgotten'' by pruning deep neural networks, we naturally extend network pruning to our GCL framework for mining minority nodes. Based on this, we co-train two pruned encoders (e.g., GNN and text encoder) in different modalities by pushing the corresponding node-text pairs together and the irrelevant node-text pairs away. Meanwhile, we propose intra-modality GCL by co-training non-pruned GNN and pruned GNN, to ensure node embeddings with similar attribute features stay closed. Last, we fine-tune the GNN encoder on downstream class-imbalanced node classification tasks. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baseline models and learns more balanced representations on real-world graphs. Our source code is available at https://github.com/graphprojects/CM-GCL.

Knowledge graphs (KGs) are serving as important resources for many applications, such as semantic search, question answering, or dialogue generation. As one of the fundamental tasks, multi-hop KG reasoning aims at generating effective and explainable relation prediction through reasoning paths. The current methods often require sufficient amount of training data (i.e., fact triples) for each query relation, impairing their applicabilities and performances over few-shot relations (with limited data) which are common in KGs. Despite that some few-shot relation reasoning methods have been proposed, their effectiveness and efficiency remain to be improved. To address these challenges, we propose a novel model called ADK-KG for multi-hop few-shot relation reasoning over KGs. In ADK-KG, we introduce a reinforcement learning framework to model the sequential reasoning process. We further develop a text-enhanced heterogeneous graph neural network to encode node embeddings, where entity and relation embeddings are pre-trained using content information. Later, we employ a task-aware meta-learning algorithm to optimize the model parameters that could be fast adapted for few-shot relations. A knowledge distillation module is further designed to make use of unlabeled data for improving model training. Extensive experiments on three benchmark datasets demonstrate that ADK-KG has satisfactory efficiency and outperforms state-of-the-art approaches.

As cyberattacks caused by malware have proliferated during the pandemic, building an automatic system to detect COVID-19 themed malware in social coding platforms is in urgent need. The existing methods mainly rely on file content analysis while ignoring structured information among entities in social coding platforms. Additionally, they usually require sufficient data for model training, impairing their performances over cases with limited data which is common in reality. To address these challenges, we develop Meta-AHIN, a novel model for COVID-19 themed malicious repository detection in GitHub. In Meta-AHIN, we first construct an attributed heterogeneous information network (AHIN) to model the code content and social coding properties in GitHub; and then we exploit attention-based graph convolutional neural network (AGCN) to learn repository embeddings and present a meta-learning framework for model optimization. To utilize unlabeled information in AHIN and to consider task influence of different types of repositories, we further incorporate node attribute-based self-supervised module and task-aware attention weight into AGCN and meta-learning respectively. Extensive experiments on the collected data from GitHub demonstrate that Meta-AHIN outperforms state-of-the-art methods.

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.

Despite the persistent effort by law enforcement, illicit drug trafficking in darknet markets has shown great resilience with new markets rapidly appearing after old ones being shut down. In order to more effectively detect, disrupt and dismantle illicit drug trades, there’s an imminent need to gain a deeper understanding toward the operations and dynamics of illicit drug trading activities. To address this challenge, in this paper, we design and develop an intelligent system (named dSytle-GAN) to automate the analysis for drug identification in darknet markets, by considering both content-based and style-aware information. To determine whether a given pair of posted drugs are the same or not, in dStyle-GAN, based on the large-scale data collected from darknet markets, we first present an attributed heterogeneous information network (AHIN) to depict drugs, vendors, texts and writing styles, photos and photography styles, and the rich relations among them; and then we propose a novel generative adversarial network (GAN) based model over AHIN to capture the underlying distribution of posted drugs’ writing and photography styles to learn robust representations of drugs for their identifications. Unlike existing approaches, our proposed GAN-based model jointly considers the heterogeneity of network and relatedness over drugs formulated by domain-specific meta-paths for robust node (i.e., drug) representation learning. To the best of our knowledge, the proposed dStyle-GAN represents the first principled GAN-based solution over graphs to simultaneously consider writing and photography styles as well as their latent distributions for node representation learning. Extensive experimental results based on large-scale datasets collected from six darknet markets and the obtained ground-truth demonstrate that dStyle-GAN outperforms the state-of-the-art methods. Based on the identified drug pairs in the wild by dStyle-GAN, we perform further analysis to gain deeper insights into the dynamics and evolution of illicit drug trading activities in darknet markets, whose findings may facilitate law enforcement for proactive interventions.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond with actionable strategies for community mitigation, leveraging the large-scale and real-time pandemic related data generated from heterogeneous sources (e.g., disease related data, demographic data, mobility data, and social media data), in this work, we propose and develop a data-driven system (named α-satellite), as an initial offering, to provide real-time COVID-19 risk assessment in a hierarchical manner in the United States. More specifically, given a location (either user input or automatic positioning), the system will automatically provide risk indices associated with the specific location, the county that location is in and the state as a whole to enable people to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. In α-satellite, we first construct an attributed heterogeneous information network (AHIN) to model the collected multi-source data in a comprehensive way; and then we utilize meta-path based schemes to model both vertical and horizontal information associated with a given location (i.e., point of interest, POI); finally we devise a novel heterogeneous graph neural network to aggregate its neighborhood information to estimate the risk of the given POI in a hierarchical manner. To comprehensively evaluate the performance of α-satellite in real-time COVID-19 risk assessment, a set of studies are first performed to validate its utility; based on a real-world dataset consisting of 6,538 annotated POIs, the experimental results show that α-satellite achieves the area of under curve (AUC) of 0.9378, which outperforms the state-of-the-art baselines. After we launched the system for public tests, it had attracted 51,190 users as of May 30. Based on the analysis of its large-scale users, we have a key finding that people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using the system for actionable information. Our system and generated benchmark datasets have been made publicly accessible through our website.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g., disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5,060 annotated POIs, which achieves the area of under curve (AUC) of 0.9202. As of June 18, 2020, α-Satellite has had 56,980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicate the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website.