| Train your own gnn teacher: Graph-aware distillation on textual graphs C Mavromatis, VN Ioannidis, S Wang, D Zheng, S Adeshina, J Ma, H Zhao, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 18 | 2023 |
| Page-link: Path-based graph neural network explanation for heterogeneous link prediction S Zhang, J Zhang, X Song, S Adeshina, D Zheng, C Faloutsos, Y Sun Proceedings of the ACM Web Conference 2023, 3784-3793, 2023 | 16 | 2023 |
| Does your graph need a confidence boost? convergent boosted smoothing on graphs with tabular node features J Chen, J Mueller, VN Ioannidis, S Adeshina, Y Wang, T Goldstein, D Wipf International Conference on Learning Representations, 2021 | 14 | 2021 |
| Relational graph neural networks for fraud detection in a super-app environment JD Acevedo-Viloria, L Roa, S Adeshina, CC Olazo, A Rodríguez-Rey, ... arXiv preprint arXiv:2107.13673, 2021 | 9 | 2021 |
| Convergent boosted smoothing for modeling graph data with tabular node features J Chen, J Mueller, VN Ioannidis, S Adeshina, Y Wang, T Goldstein, D Wipf arXiv preprint arXiv:2110.13413, 2021 | 5 | 2021 |
| Orthoreg: Improving graph-regularized mlps via orthogonality regularization H Zhang, S Wang, VN Ioannidis, S Adeshina, J Zhang, X Qin, C Faloutsos, ... arXiv preprint arXiv:2302.00109, 2023 | 3 | 2023 |
| Scalable consistency training for graph neural networks via self-ensemble self-distillation C Hawkins, VN Ioannidis, S Adeshina, G Karypis arXiv preprint arXiv:2110.06290, 2021 | 1 | 2021 |
| NetInfoF Framework: Measuring and Exploiting Network Usable Information MC Lee, H Yu, J Zhang, VN Ioannidis, X Song, S Adeshina, D Zheng, ... arXiv preprint arXiv:2402.07999, 2024 | | 2024 |
| Credit risk modeling with graph machine learning S Das, X Huang, S Adeshina, P Yang, L Bachega INFORMS Journal on Data Science 2 (2), 197-217, 2023 | | 2023 |
| ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning DAI Zhenwei, V Ioannidis, S Adeshina, Z Jost, C Faloutsos, G Karypis Learning on Graphs Conference, 35: 1-35: 15, 2022 | | 2022 |
| PropInit: Scalable inductive initialization for heterogeneous graph neural networks S Adeshina, J Zhang, M Kim, M Chen, R Fathony, A Vashisht, J Chen, ... 2022 IEEE International Conference on Knowledge Graph (ICKG), 6-13, 2022 | | 2022 |
| Conditional Invariances for Conformer Invariant Protein Representations B Srinivasan, VN Ioannidis, S Adeshina, M Kakodkar, G Karypis, ... | | 2022 |
| ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning Z Dai, V Ioannidis, S Adeshina, Z Jost, C Faloutsos, G Karypis arXiv preprint arXiv:2206.04255, 2022 | | 2022 |
