bhooi.github.io
Open in
urlscan Pro
2606:50c0:8000::153
Public Scan
URL:
https://bhooi.github.io/
Submission: On July 24 via manual from SG — Scanned from SG
Submission: On July 24 via manual from SG — Scanned from SG
Form analysis 0 forms found in the DOM
Text Content
Toggle navigation * News * Research * Code * CV Bryan Hooi Assistant Professor National University of Singapore E-mail: bhooi@comp.nus.edu.sg Google Scholar | Twitter I'm an Assistant Professor in the School of Computing and the Institute of Data Science in National University of Singapore. My research aims to make machine learning systems more reliable and applicable to a wider variety of real-world contexts, particularly: * Trustworthiness. How do we make models more reliable and factual, and more aware of what they don't know, mitigating issues like hallucination, distribution shift, and biases? * Graphs and Structured Data. As foundation models are applied to increasingly diverse and multimodal tasks in our daily lives, they will increasingly encounter complex structured data such as graphs. How can foundation models enhance graph learning, such as for recommendation and e-commerce? * Applications. I am also interested in some other real-world applications, particularly related to web safety (scams, fraud, and misinformation) and biomedical applications. I am actively recruiting postdoctoral fellows as well as NUS students of all levels interested in the above topics. Please email me if you are interested. I received my Ph.D. in Machine Learning from Carnegie Mellon University, where I was advised by Christos Faloutsos. I received my M.S. in Computer Science and my B.S. in Mathematics from Stanford University. -------------------------------------------------------------------------------- Students * Yifan Zhang * Jiaying Wu * Ailin Deng * Miao Xiong * Xiaoxin He (co-advised with Xavier Bresson) * Zhiyuan Hu (co-advised with Ng See Kiong) * Yuan Li (co-advised with He Bingsheng) * Sara Bakić (co-advised with Mile Sikic) * Ivona Martinović (co-advised with Mile Sikic) * Yufei He * Yuan Sui Research 2024 * KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection Yuexin Li, Chengyu Huang, Shumin Deng, Mei Lin Lock, Tri Cao, Nay Oo, Bryan Hooi, Hoon Wei Lim USENIX Security 2024 * Fake News in Sheep’s Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks Jiaying Wu, Bryan Hooi KDD 2024 * Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View Jintian Zhang, Xin Xu, Ningyu Zhang, Ruibo Liu, Bryan Hooi, Shumin Deng ACL 2024 * Truth Table Net: Scalable, Compact & Verifiable Neural Networks with a Dual Convolutional Small Boolean Circuit Networks Form Adrien Benamira, Thomas Peyrin, Trevor Yap, Tristan Guérand, Bryan Hooi IJCAI 2024 * Interrelated Dense Pattern Detection in Multilayer Networks Wenjie Feng , Li Wang, Bryan Hooi, See Kiong Ng, Shenghua Liu TKDE 2024 * UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language Yufei He, Bryan Hooi arXiv 2024 * G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi arXiv 2024 * Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding Ailin Deng, Zhirui Chen, Bryan Hooi Workshop on Reliable and Responsible Foundation Models, ICLR 2024 * Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models Zhiyuan Hu, Chumin Liu, Xidong Feng, Yilun Zhao, See-Kiong Ng, Anh Tuan Luu, Junxian He, Pang Wei Koh, Bryan Hooi Workshop on LLM Agents, ICLR 2024 * UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, Roger Zimmermann WebConf 2024 * Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, Bryan Hooi ICLR 2024 * Scalable and Effective Implicit Graph Neural Networks on Large Graphs Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Yiwei Wang, Chaosheng Dong, Xiaokui Xiao ICLR 2024 * Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, Bryan Hooi ICLR 2024 * Partitioning Message Passing for Graph Fraud Detection Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen ICLR 2024 * Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, Jia Chen ICLR 2024 (Spotlight) * PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation Zhiyuan Hu, Chumin Liu, Yue Feng, Anh Tuan Luu, Bryan Hooi AAAI 2024 * MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering Jun Hu, Bryan Hooi, Shengsheng Qian, Quan Fang, Changsheng Xu TKDE 2024 2023 * HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation Yifan Zhang, Bryan Hooi arXiv 2023 * Towards A Unified View of Answer Calibration for Multi-Step Reasoning Shumin Deng, Ningyu Zhang, Nay Oo, Bryan Hooi arXiv 2023 * Proximity-Informed Calibration for Deep Neural Networks Miao Xiong, Ailin Deng, Pang Wei Koh, Jiaying Wu, Shen Li, Jianqing Xu, Bryan Hooi NeurIPS 2023 (Spotlight) * Expanding Small-Scale Datasets with Guided Imagination Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, Jiashi Feng NeurIPS 2023 * Primacy Effect of ChatGPT Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang, Bryan Hooi EMNLP 2023 * How Fragile is Relation Extraction under Entity Replacements? Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, Muhao Chen CoNLL 2023 * LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann NeurIPS 2023 (Datasets and Benchmarks Track) * TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks Baixiang Huang, Bryan Hooi and Kai Shu SIGSPATIAL 2023 * Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection Jiaying Wu, Shen Li, Ailin Deng, Miao Xiong, Bryan Hooi CIKM 2023 * Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani CIKM 2023 (Short Paper) * DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection Jiaying Wu, Bryan Hooi KDD 2023 * Sketch-Based Anomaly Detection in Streaming Graphs Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi KDD 2023 * SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres Shumin Deng, Shengyu Mao, Ningyu Zhang, Bryan Hooi ACL 2023 * Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement Ailin Deng, Miao Xiong, Bryan Hooi ICML 2023 * A Generalization of ViT/MLP-Mixer to Graphs Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, Xavier Bresson ICML 2023 * Reachability-Aware Laplacian Representation in Reinforcement Learning Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang ICML 2023 * GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks Yuwen Li, Miao Xiong, Bryan Hooi ICML 2023 * Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering Mingqi Yang, Wenjie Feng, Yanming Shen, Bryan Hooi ICML 2023 * Deep Long-Tailed Learning: A Survey Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, Jiashi Feng TPAMI 2023 * Probabilistic Knowledge Distillation for Face Ensembles Jianqing Xu, Shen Li, Ailin Deng, Miao Xiong, Jiaying Wu, Jiaxiang Wu, Shouhong Ding, Bryan Hooi CVPR 2023 * Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph Shumin Deng, Chengming Wang, Zhoubo Li, Ningyu Zhang, Zelin Dai, Hehong Chen, Feiyu Xiong, Ming Yan, Qiang Chen, Mosha Chen, Jiaoyan Chen, Jeff Z. Pan, Bryan Hooi, Huajun Chen ICDE 2023 * Spade: A real-time fraud detection framework on evolving graphs Jiaxin Jiang, Yuan Li, Bingsheng He, Bryan Hooi, Jia Chen, Johan Kok Zhi Kang VLDB 2023 * Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference Yiwei Wang, Bryan Hooi, Yozen Liu, Neil Shah WSDM 2023 2022 * Flashlight: Scalable Link Prediction with Effective Decoders Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah LoG 2022 * Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang LoG 2022 * Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng NeurIPS 2022 * MGNNI: Multiscale Graph Neural Networks with Implicit Layers Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao NeurIPS 2022 * Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation Miao Xiong, Shen Li, Wenjie Feng, Ailin Deng, Jihai Zhang, Bryan Hooi TMLR 2022 * LPGNet: Link Private Graph Networks for Node Classification Aashish Kolluri, Teodora Baluta, Bryan Hooi, Prateek Saxena ACM CCS 2022 * When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting? Xu Liu, Yuxuan Liang, Chao Huang, Yu Zheng, Bryan Hooi and Roger Zimmermann SIGSPATIAL 2022 * Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, Bryan Hooi ECCV 2022 * Time-Aware Neighbor Sampling on Temporal Graphs Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Bryan Hooi IJCNN 2022 * Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks Jiaying Wu, Bryan Hooi ECML-PKDD 2022 * LSCALE: Latent Space Clustering-Based Active Learning for Node Classification Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao ECML-PKDD 2022 * ARES: Locally Adaptive Reconstruction-based Anomaly Scoring Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng ECML-PKDD 2022 * The Geometry of Robust Value Functions Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor ICML 2022 * Neural PCA for Flow-Based Representation Learning Shen Li, Bryan Hooi IJCAI 2022 * CADET: Calibrated Anomaly Detection for Mitigating Hardness Bias Ailin Deng, Adam Goodge, Lang Yi Ang, Bryan Hooi IJCAI 2022 * Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi NAACL 2022 * Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng, Rui Tan SIGIR 2022 * MemStream: Memory-Based Streaming Anomaly Detection Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi WebConf 2022 * LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng AAAI 2022 * Time Series Anomaly Detection with Adversarial Reconstruction Networks Shenghua Liu, Bin Zhou, Quan Ding, Bryan Hooi, Zhengbo Zhang, Huawei Shen, Xueqi Cheng IEEE TKDE 2022 * Dynamic Graph-Based Anomaly Detection in the Electrical Grid Shimiao Li, Amritanshu Pandey, Bryan Hooi, Christos Faloutsos, Larry Pileggi IEEE Transactions on Power Systems 2022 * MonLAD: Money Laundering Agents Detection in Transaction Streams Xiaobing Sun, Wenjie Feng, Shenghua Liu, Yuyang Xie, Siddharth Bhatia, Bryan Hooi, Wenhan Wang, Xueqi Cheng WSDM 2022 * Real-Time Anomaly Detection in Edge Streams Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos TKDD 2022 2021 * Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng NeurIPS 2021 * EIGNN: Efficient Infinite-Depth Graph Neural Networks Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao NeurIPS 2021 * Adaptive Data Augmentation on Temporal Graphs Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi NeurIPS 2021 * SSMF: Shifting Seasonal Matrix Factorization Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi NeurIPS 2021 * Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks Kuangqi Zhou*, Yanfei Dong*, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng CIKM 2021 * GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs Siddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty ECML-PKDD 2021 * Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing Kaixin Wang*, Kuangqi Zhou*, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng ICML 2021 * PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration Shixuan Sun, Yuhang Chen, Bingsheng He, and Bryan Hooi SIGMOD 2021 * Spherical Confidence Learning for Face Recognition Shen Li, Xu Jianqing, Xiaqing Xu, Pengcheng Shen, Shaoxin Li, and Bryan Hooi CVPR 2021 * Mixup for Node and Graph Classification Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi WebConf 2021 * CurGraph: Curriculum Learning for Graph Classification Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi WebConf 2021 * MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar and Bryan Hooi WebConf 2021, Best paper finalist. * ExGAN: Adversarial Generation of Extreme Samples Siddharth Bhatia*, Arjit Jain*, Bryan Hooi AAAI 2021 * Graph Neural Network-Based Anomaly Detection in Multivariate Time Series Ailin Deng, Bryan Hooi AAAI 2021 * Origin-Aware Next Destination Recommendation with Personalized Preference Attention Nicholas Lim, Bryan Hooi, See Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng and Rui Tan WSDM 2021 2020 * Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off Minji Yoon, Theophile Gervet, Bryan Hooi, and Christos Faloutsos ICDM 2020, Selected as one of the best papers of ICDM’20 for journal invitation to special issue of KAIS * Provably Robust Node Classification via Low-Pass Message Passing Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, and Bryan Hooi ICDM 2020 * Detecting Implementation Bugs in Graph Convolutional Network based Node Classifiers Yiwei Wang, Wei Wang, Yujun Cai, Bryan Hooi and Beng Chin Ooi ISSTA 2020 * STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation Nicholas Lim, Bryan Hooi, See Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng and Jagannadan Varadarajan CIKM 2020 * Progressive Supervision for Node Classification Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi ECML-PKDD 2020 * NodeAug: Semi-Supervised Node Classification with Data Augmentation Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu and Bryan Hooi KDD 2020 * Structural Patterns and Generative Models of Real-world Hypergraphs Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin KDD 2020 * Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng IJCAI 2020 * Identifying through Flows for Recovering Latent Representations Shen Li, Bryan Hooi, Gim Hee Lee ICLR 2020 * FlowScope: Spotting Money Laundering Based on Graphs Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng AAAI 2020 * TellTail: Fast Scoring and Detection of Dense Subgraphs Bryan Hooi, Kijung Shin, Hemank Lamba, and Christos Faloutsos AAAI 2020 * MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos AAAI 2020 * Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams Kijung Shin, Sejoon Oh, Jisu Kim, Bryan Hooi, and Christos Faloutsos TKDD 2020 2019 * BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye IJCAI 2019 * Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach Minji Yoon, Bryan Hooi, Kijung Shin and Christos Faloutsos KDD 2019 * Impact of Load Models on Power Flow Optimization Marko Jereminov, Bryan Hooi, Amritanshu Pandey, Hyun Ah Song, Christos Faloutsos, and Larry Pileggi IEEE Power & Energy Society General Meeting (PES) 2019 * SMF: Drift Aware Matrix Factorization with Seasonal Patterns Bryan Hooi, Kijung Shin, Shenghua Liu, and Christos Faloutsos SDM, 2019 * Branch and Border: Partition-Based Change Detection in Multivariate Time Series Bryan Hooi and Christos Faloutsos SDM, 2019 * Anomaly Detection in Large Graphs based on Vision-guided Summarization Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, Xueqi Cheng PAKDD, 2019 2018 * ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph. Bryan Hooi, Dhivya Eswaran, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos CIKM, 2018 * A Contrast Metric for Fraud Detection in Rich Graphs Shenghua Liu, Bryan Hooi, and Christos Faloutsos TKDE 2018 * GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid. Bryan Hooi, Dhivya Eswaran, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos ECML-PKDD 2018, Runner-up Best Student Data Mining Paper Award * Think before You Discard: Accurate Triangle Counting in Graph Streams with Deletions. Kijung Shin, Jisu Kim, Bryan Hooi, and Christos Faloutsos ECML-PKDD 2018 * ONE-M: Modeling the Co-evolution of Opinions and Network Connections Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla ECML-PKDD 2018 * Fast, Accurate and Flexible Algorithms for Dense Subtensor Mining. Kijung Shin, Bryan Hooi, and Christos Faloutsos TKDD 2018 * NeuCast: Seasonal Neural Forecast of Power Grid Time Series. Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng IJCAI 2018 * StreamCast: Fast and Online Mining of Power Grid Time Sequences. Bryan Hooi, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos SDM, 2018 * REV2: Fraudulent User Prediction in Rating Platforms. Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and V.S. Subrahmanian WSDM 2018 2017 * EyeQual: Accurate, Explainable, Retinal Image Quality Assessment. Pedro Costa, Aurelio Campilho, Bryan Hooi, Asim Smailagic, Kris Kitani, Shenghua Liu, Christos Faloutsos and Adrian Galdran IEEE ICMLA 2017 * HoloScope: Topology-and-Spike Aware Fraud Detection. Shenghua Liu, Bryan Hooi, Christos Faloutsos CIKM 2017 * ZOORANK: Ranking Suspicious Entities in Time-Evolving Tensors. Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, and Jurgen Pfeffer ECML-PKDD 2017 * BEATLEX: Summarizing and Forecasting Time Series with Patterns. Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos ECML-PKDD 2017 * PowerCast: Mining and Forecasting Power Grid Sequences. Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Larry Pileggi, and Christos Faloutsos ECML-PKDD 2017 * The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data. Chi Ling Chan, Justin Lai, Bryan Hooi and Todd Davies SocInfo 2017 * Linear Load Model for Robust Power System Analysis. Marko Jereminov, Amritanshu Pandey, Hyun Ah Song, Bryan Hooi, Larry Pileggi, and Christos Faloutsos ISGT 2017 * DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams. Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos KDD 2017 * Graph-Based Fraud Detection in the Face of Camouflage. Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, and Christos Faloutsos TKDD, 2017 * AutoCyclone: Automatic Mining of Cyclic Patterns and Anomalies by Robust Tensor Factorization. Tsubasa Takahashi, Bryan Hooi, and Christos Faloutsos WebConf 2017 * D-Cube: Dense-Block Detection in Terabyte-Scale Tensors. Kijung Shin, Bryan Hooi, Jisu Kim and Christos Faloutsos WSDM 2017 2016 * M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees. Kijung Shin, Bryan Hooi, and Christos Faloutsos ECML-PKDD 2016 * FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos KDD 2016, KDD Best Paper Award (Research Track) * Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algortihms. Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos TKDE 2016 * Power-Hop: A Pervasive Observation for Real Complex Networks. Evangelos E. Papalexakis, Bryan Hooi, Konstantinos Pelechrinis, and Christos Faloutsos PLoS ONE 11(3) 2016 * BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, and Christos Faloutsos SDM 2016 * Matrices, Compression, Learning Curves: formulation, and the GROUPNTEACH algorithms. Bryan Hooi, Hyun Ah Song, Evangelos Papalexakis, Rakesh Agrawal, and Christos Faloutsos PAKDD 2016 2015 * A General Suspiciousness Metric for Dense Blocks in Multimodal Data. Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos ICDM 2015 -------------------------------------------------------------------------------- Code Branch and Border: Partition-Based Change Detection in Multivariate Time Series. BNB (Branch and Border) is a nonparametric multivariate change detection method. BNB approaches change detection by separating points before and after the change using an ensemble of random partitions. BeatLex: Summarizing and Forecasting Time Series with Patterns. BeatLex is an algorithm that succintly summarizes and forecasts time series data. It is designed for data containing patterns that occur repeatedly, especially if these patterns are complex and nonlinear, change over time, and may distortions in their shape or length. FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. FRAUDAR is an algorithm for detecting graph fraud based on dense subgraph detection, which is aimed at being robust to camouflage (i.e. attackers which add false edges in order to mask their presence) BIRDNEST: Bayesian Inference for Ratings-Fraud Detection BIRDNEST is an algorithm for detecting fraudulent users in timestamped ratings data (e.g. users ratings products with 1 to 5 stars), based on detecting users that differ excessively from the norm, in terms of what ratings they give and the time distribution of their ratings. Matrices, Compression, Learning Curves: formulation, and the GROUPNTEACH algorithms. GNT takes a collection of facts, arranged in a binary matrix, and reorders the rows and columns for the purpose of teaching and visualization.