93 citations · 476 across the 19 of their papers we have counts for
11 papers · 1 filter
DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection
Jiaying Wu, Bryan Hooi
Recent efforts in fake news detection have witnessed a surge of interest in using graph neural networks (GNNs) to exploit rich social context. Existing studies generally leverage f…
Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks
Jiaying Wu, Bryan Hooi
As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets.…
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Jun Hu, Bryan Hooi, Shengsheng Qian +2
Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. Howeve…
MonLAD: Money Laundering Agents Detection in Transaction Streams
Xiaobing Sun, Wenjie Feng, Shenghua Liu +5
Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering…
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
Minji Yoon, Bryan Hooi, Kijung Shin +1
Given a dynamic graph stream, how can we detect the sudden appearance of anomalous patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can w…
Structural Patterns and Generative Models of Real-world Hypergraphs
Manh Tuan Do, Se-eun Yoon, Bryan Hooi +1
Graphs have been utilized as a powerful tool to model pairwise relationships between people or objects. Such structure is a special type of a broader concept referred to as hypergr…
EagleMine: Vision-Guided Mining in Large Graphs
Wenjie Feng, Shenghua Liu, Christos Faloutsos +3
Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics, and how can we detect them and separate anomalous nodes in a way similar to…
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams
Kijung Shin, Bryan Hooi, Jisu Kim +1
Consider a stream of retweet events - how can we spot fraudulent lock-step behavior in such multi-aspect data (i.e., tensors) evolving over time? Can we detect it in real time, wit…
HoloScope: Topology-and-Spike Aware Fraud Detection
Shenghua Liu, Bryan Hooi, Christos Faloutsos
As online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, fraudulent attacks become less obvious and their detection bec…
FairJudge: Trustworthy User Prediction in Rating Platforms
Srijan Kumar, Bryan Hooi, Disha Makhija +3
Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive mo…
EdgeCentric: Anomaly Detection in Edge-Attributed Networks
Neil Shah, Alex Beutel, Bryan Hooi +5
Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce net…