| Beyond homophily in graph neural networks: Current limitations and effective designs J Zhu, Y Yan, L Zhao, M Heimann, L Akoglu, D Koutra Proceedings of the 34th Annual Conference on Neural Information Processing …, 2020 | 851 | 2020 |
| Regal: Representation learning-based graph alignment M Heimann, H Shen, T Safavi, D Koutra Proceedings of the 27th ACM international conference on information and …, 2018 | 303 | 2018 |
| CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding X Chen, M Heimann, F Vahedian, D Koutra Proceedings of the 29th ACM International Conference on Information …, 2020 | 54 | 2020 |
| node2bits: Compact Time-and Attribute-aware Node Representations for User Stitching D Jin, M Heimann, R Rossi, D Koutra ECML/PKDD European Conference on Principles and Practice of Knowledge …, 2019 | 45 | 2019 |
| On generalizing neural node embedding methods to multi-network problems M Heimann, D Koutra KDD MLG Workshop 58, 2017 | 36 | 2017 |
| G-crewe: Graph compression with embedding for network alignment KK Qin, FD Salim, Y Ren, W Shao, M Heimann, D Koutra Proceedings of the 29th ACM International Conference on Information …, 2020 | 30 | 2020 |
| Toward understanding and evaluating structural node embeddings J Jin, M Heimann, D Jin, D Koutra ACM Transactions on Knowledge Discovery from Data (TKDD) 16 (3), 1-32, 2021 | 26* | 2021 |
| Hashalign: Hash-based alignment of multiple graphs M Heimann, W Lee, S Pan, KY Chen, D Koutra Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia …, 2018 | 24 | 2018 |
| Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding J Zhu, X Lu, M Heimann, D Koutra Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 23 | 2021 |
| Smart Roles: Inferring Professional Roles in Email Networks D Jin, M Heimann, T Safavi, M Wang, W Lee, L Snider, D Koutra Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 22 | 2019 |
| Distribution of Node Embeddings as Multiresolution Features for Graphs M Heimann, T Safavi, D Koutra Proceedings of the 19th IEEE International Conference on Data Mining (ICDM), 2019 | 19 | 2019 |
| Analyzing data-centric properties for graph contrastive learning P Trivedi, ES Lubana, M Heimann, D Koutra, J Thiagarajan Advances in Neural Information Processing Systems 35, 14030-14043, 2022 | 16* | 2022 |
| Refining network alignment to improve matched neighborhood consistency M Heimann, X Chen, F Vahedian, D Koutra Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 13 | 2021 |
| On graph neural network fairness in the presence of heterophilous neighborhoods D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra arXiv preprint arXiv:2207.04376, 2022 | 7* | 2022 |
| Heterophily and graph neural networks: Past, present and future J Zhu, Y Yan, M Heimann, L Zhao, L Akoglu, D Koutra IEEE Data Engineering Bulletin, 2023 | 6 | 2023 |
| Caper: Coarsen, align, project, refine-a general multilevel framework for network alignment J Zhu, D Koutra, M Heimann Proceedings of the 31st ACM International Conference on Information …, 2022 | 6 | 2022 |
| Fast flow-based random walk with restart in a multi-query setting Y Yan, M Heimann, D Jin, D Koutra Proceedings of the 2018 SIAM International Conference on Data Mining, 342-350, 2018 | 6 | 2018 |
| Exploring classification of topological priors with machine learning for feature extraction S Leventhal, A Gyulassy, M Heimann, V Pascucci IEEE Transactions on Visualization and Computer Graphics, 2023 | 4 | 2023 |
| Contrastive knowledge-augmented meta-learning for few-shot classification R Subramanyam, M Heimann, TS Jayram, R Anirudh, JJ Thiagarajan Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 3 | 2023 |
| Structural node embedding in signed social networks: Finding online misbehavior at multiple scales M Heimann, G Murić, E Ferrara Complex Networks & Their Applications IX: Volume 2, Proceedings of the Ninth …, 2021 | 3 | 2021 |
Danai KoutraUniversity of Michigan, Ann Arbor在 umich.edu 的电子邮件经过验证
