Preprint Descriptive Kernel Convolution Network with Improved Random Walk Kernel 2024 Lee M-C, Zhao L, Akoglu L
Preprint End-To-End Self-tuning Self-supervised Time Series Anomaly Detection 2024 Deforce B, Lee M-C, Baesens B, Asensio ES, Yoo J, Akoglu L
Preprint Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation 2024 Zhao L, Ding X, Akoglu L
Conference 19th International Workshop on Mining and Learning with Graphs (MLG) 2023 • Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining • 5882-5883 Shah N, Fakhraei S, Zheng D, Fatemi B, Akoglu L
Journal Article A Comprehensive Survey on Graph Anomaly Detection With Deep Learning 2023 • IEEE Transactions on Knowledge and Data Engineering • 35(12):12012-12038 Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L
Preprint ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach 2023 Sotiropoulos K, Zhao L, Liang PJ, Akoglu L
Journal Article Benefit-aware early prediction of health outcomes on multivariate EEG time series 2023 • Journal of Biomedical Informatics • 139: Shekhar S, Eswaran D, Hooi B, Elmer J, Faloutsos C, Akoglu L
Journal Article Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success 2023 Akoglu L, Yoo J, Zhao T
Journal Article Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection 2023 • IEEE Intelligent Systems • 38(2):32-35 Xia F, Akoglu L, Aggarwal C, Liu H
Journal Article Density of states for fast embedding node-attributed graphs 2023 • Knowledge and Information Systems • 65(6):2455-2483 Zhao L, Sawlani S, Akoglu L
Journal Article Detecting Anomalous Graphs in Labeled Multi-Graph Databases 2023 • ACM Transactions on Knowledge Discovery from Data • 17(2): Nguyen HT, Liang PJ, Akoglu L
Preprint DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection 2023 Yoo J, Zhao Y, Zhao L, Akoglu L
Chapter DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection 2023 • Lecture Notes in Computer Science • 14169:254-269 Yoo J, Zhao Y, Zhao L, Akoglu L
Preprint End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection 2023 Yoo J, Zhao L, Akoglu L
Preprint Fast Unsupervised Deep Outlier Model Selection with Hypernetworks 2023 Ding X, Zhao Y, Akoglu L
Conference From Detection to Action: a Human-in-the-loop Toolkit for Anomaly Reasoning and Management 2023 • PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023 • 279-287 Ding X, Seleznev N, Kumar S, Bruss CB, Akoglu L
Preprint From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management 2023 Ding X, Seleznev N, Kumar S, Bruss CB, Akoglu L
Journal Article On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights 2023 • Big Data • 11(3):151-180 Zhao L, Akoglu L
Journal Article PC Chairs' Welcome 2023 • Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining • IV Akoglu L, Gunopulos D, Yan X, Kumar R, Ozcan F, Ye J
Preprint Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities 2023 Akoglu L, Yoo J
Chapter Summarizing Labeled Multi-graphs 2023 • Lecture Notes in Computer Science • 13714:53-68 Berberidis D, Liang PJ, Akoglu L
Journal Article The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies 2023 • ACM SIGKDD Explorations Newsletter • 25(1):19-35 Q. M, Zhao Y, Zhang X, Akoglu L
Conference A Practical, Progressively-Expressive GNN 2022 • Advances in Neural Information Processing Systems Zhao L, Haertel L, Shah N, Akoglu L
