Leman Akoglu
Affiliations: | Computer Science | Stony Brook University, Stony Brook, NY, United States |
Area:
Mining and Modeling of Large-scale Real-world Networks, Social Network Analysis, Machine Learning, Graph Mining, Pattern Discovery, Anomaly and Event Detection.Google:
"Leman Akoglu"
BETA: Related publications
See more...
Publications
|
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
| Zhao L, Akoglu L. (2021) On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights. Big Data |
| Perozzi B, Akoglu L. (2018) Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization Acm Transactions On Knowledge Discovery From Data. 12: 24 |
| Macha M, Akoglu L. (2018) Explaining anomalies in groups with characterizing subspace rules Data Mining and Knowledge Discovery. 32: 1444-1480 |
| Vidros S, Kolias C, Kambourakis G, et al. (2017) Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset Future Internet. 9: 6 |
| Vlasselaer VV, Eliassi-Rad T, Akoglu L, et al. (2017) GOTCHA! Network-Based Fraud Detection for Social Security Fraud Management Science. 63: 3090-3110 |
| Rayana S, Akoglu L. (2016) Less is More: Building Selective Anomaly Ensembles Acm Transactions On Knowledge Discovery From Data. 10: 42 |
| Chan H, Akoglu L. (2016) Optimizing network robustness by edge rewiring: a general framework Data Mining and Knowledge Discovery. 1-31 |
| Van Vlasselaer V, Akoglu L, Eliassi-Rad T, et al. (2015) Guilt-by-constellation: Fraud detection by suspicious clique memberships Proceedings of the Annual Hawaii International Conference On System Sciences. 2015: 918-927 |
| Yao Y, Tong H, Xie T, et al. (2015) Detecting high-quality posts in community question answering sites Information Sciences. 302: 70-82 |
| Akoglu L, Tong H, Koutra D. (2015) Graph based anomaly detection and description: A survey Data Mining and Knowledge Discovery. 29: 626-688 |