| Graph based anomaly detection and description: a survey L Akoglu, H Tong, D Koutra Data mining and knowledge discovery 29, 626-688, 2015 | 1820 | 2015 |
| Beyond homophily in graph neural networks: Current limitations and effective designs J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra Advances in neural information processing systems 33, 7793-7804, 2020 | 1142 | 2020 |
| Oddball: Spotting anomalies in weighted graphs L Akoglu, M McGlohon, C Faloutsos Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia …, 2010 | 774 | 2010 |
| Collective opinion spam detection: Bridging review networks and metadata S Rayana, L Akoglu Proceedings of the 21th acm sigkdd international conference on knowledge …, 2015 | 763 | 2015 |
| A comprehensive survey on graph anomaly detection with deep learning X Ma, J Wu, S Xue, J Yang, C Zhou, QZ Sheng, H Xiong, L Akoglu IEEE transactions on knowledge and data engineering 35 (12), 12012-12038, 2021 | 726 | 2021 |
| Pairnorm: Tackling oversmoothing in gnns L Zhao, L Akoglu arXiv preprint arXiv:1909.12223, 2019 | 661 | 2019 |
| Opinion fraud detection in online reviews by network effects L Akoglu, R Chandy, C Faloutsos Proceedings of the international AAAI conference on web and social media 7 …, 2013 | 576 | 2013 |
| Rolx: structural role extraction & mining in large graphs K Henderson, B Gallagher, T Eliassi-Rad, H Tong, S Basu, L Akoglu, ... Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 570 | 2012 |
| APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions V Van Vlasselaer, C Bravo, O Caelen, T Eliassi-Rad, L Akoglu, M Snoeck, ... Decision support systems 75, 38-48, 2015 | 544 | 2015 |
| Focused clustering and outlier detection in large attributed graphs B Perozzi, L Akoglu, P Iglesias Sánchez, E Müller Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 301 | 2014 |
| Fast memory-efficient anomaly detection in streaming heterogeneous graphs E Manzoor, SM Milajerdi, L Akoglu Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 299 | 2016 |
| It's who you know: graph mining using recursive structural features K Henderson, B Gallagher, L Li, L Akoglu, T Eliassi-Rad, H Tong, ... Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 284 | 2011 |
| Gotcha! network-based fraud detection for social security fraud V Van Vlasselaer, T Eliassi-Rad, L Akoglu, M Snoeck, B Baesens Management Science 63 (9), 3090-3110, 2017 | 216 | 2017 |
| Discovering opinion spammer groups by network footprints J Ye, L Akoglu Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 209 | 2015 |
| From stars to subgraphs: Uplifting any GNN with local structure awareness L Zhao, W Jin, L Akoglu, N Shah arXiv preprint arXiv:2110.03753, 2021 | 202 | 2021 |
| Event detection in time series of mobile communication graphs L Akoglu, C Faloutsos Army science conference 1, 141-148, 2010 | 192 | 2010 |
| Scalable anomaly ranking of attributed neighborhoods B Perozzi, L Akoglu Proceedings of the 2016 SIAM international conference on data mining, 207-215, 2016 | 188 | 2016 |
| Pics: Parameter-free identification of cohesive subgroups in large attributed graphs L Akoglu, H Tong, B Meeder, C Faloutsos Proceedings of the 2012 SIAM international conference on data mining, 439-450, 2012 | 175 | 2012 |
| Less is more: Building selective anomaly ensembles S Rayana, L Akoglu Acm transactions on knowledge discovery from data (tkdd) 10 (4), 1-33, 2016 | 173 | 2016 |
| Weighted graphs and disconnected components: patterns and a generator M McGlohon, L Akoglu, C Faloutsos Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 164 | 2008 |
Christos FaloutsosCMU確認したメール アドレス: cs.cmu.edu
