93 citations · 476 across the 19 of their papers we have counts for
22 papers
Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
Jiaying Wu, Jiafeng Guo, Bryan Hooi
It is commonly perceived that fake news and real news exhibit distinct writing styles, such as the use of sensationalist versus objective language. However, we emphasize that style…
Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection
Jiaying Wu, Shen Li, Ailin Deng +2
Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of new…
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…
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Mingqi Yang, Wenjie Feng, Yanming Shen +1
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Trans…
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…
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi +3
Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible? Early decision-ma…
Deep Long-Tailed Learning: A Survey
Yifan Zhang, Bingyi Kang, Bryan Hooi +2
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-ta…
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
Shimiao Li, Amritanshu Pandey, Bryan Hooi +2
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensor…
Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
Minji Yoon, Théophile Gervet, Bryan Hooi +1
Graph data is ubiquitous in academia and industry, from social networks to bioinformatics. The pervasiveness of graphs today has raised the demand for algorithms that can answer va…
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…
Real-Time Anomaly Detection in Edge Streams
Siddharth Bhatia, Rui Liu, Bryan Hooi +3
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant tim…
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…
Impact of Load Models on Power Flow Optimization
Marko Jereminov, Bryan Hooi, Amritanshu Pandey +3
Aggregated load models, such as PQ and ZIP, are used to represent the approximated load demand at specific buses in grid simulation and optimization problems. In this paper we exam…
Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining
Kijung Shin, Bryan Hooi, Jisu Kim +1
How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i.e., tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past s…
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…
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
Bryan Hooi, Neil Shah, Alex Beutel +5
Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake rev…