| Inhomogeneous hypergraph clustering with applications P Li, O Milenkovic NeurIPS 2017, 2017 | 112 | 2017 |
| Adaptive universal generalized pagerank graph neural network E Chien, J Peng, P Li, O Milenkovic International Conference on Learning Representations. https://openreview …, 2021 | 89 | 2021 |
| Distance encoding: Design provably more powerful neural networks for graph representation learning P Li, Y Wang, H Wang, J Leskovec NeurIPS 2020, 2020 | 74* | 2020 |
| Conditional structure generation through graph variational generative adversarial nets C Yang, P Zhuang, W Shi, A Luu, P Li Advances in Neural Information Processing Systems 32, 2019 | 60 | 2019 |
| Submodular hypergraphs: p-laplacians, cheeger inequalities and spectral clustering P Li, O Milenkovic International Conference on Machine Learning, 3014-3023, 2018 | 58 | 2018 |
| Motif clustering and overlapping clustering for social network analysis P Li, H Dau, G Puleo, O Milenkovic IEEE INFOCOM 2017-IEEE Conference on Computer Communications, 1-9, 2017 | 51 | 2017 |
| Graph Information Bottleneck T Wu, H Ren, P Li, J Leskovec NeurIPS 2020, 2020 | 41 | 2020 |
| Spectrally efficient CSI acquisition for power line communications: A Bayesian compressive sensing perspective W Ding, Y Lu, F Yang, W Dai, P Li, S Liu, J Song IEEE Journal on Selected Areas in Communications 34 (7), 2022-2032, 2016 | 37 | 2016 |
| Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning M Zhang, P Li, Y Xia, K Wang, L Jin Advances in Neural Information Processing Systems 34, 2021 | 36* | 2021 |
| Meta-graph based hin spectral embedding: Methods, analyses, and insights C Yang, Y Feng, P Li, Y Shi, J Han 2018 IEEE International Conference on Data Mining (ICDM), 657-666, 2018 | 34 | 2018 |
| Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks Y Wang, YY Chang, Y Liu, J Leskovec, P Li ICLR 2021, 2021 | 29 | 2021 |
| Optimizing generalized pagerank methods for seed-expansion community detection P Li, E Chien, O Milenkovic NeurIPS 2019, 2019 | 29 | 2019 |
| Quadratic Decomposable Submodular Function Minimization: Theory and Practice. P Li, N He, O Milenkovic J. Mach. Learn. Res. 21, 106:1-106:49, 2020 | 24* | 2020 |
| Adversarial Graph Augmentation to Improve Graph Contrastive Learning S Suresh, P Li, C Hao, J Neville NeurIPS 2021, 2021 | 23 | 2021 |
| Combining decision trees and neural networks for learning-to-rank in personal search P Li, Z Qin, X Wang, D Metzler Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 18 | 2019 |
| Revisiting decomposable submodular function minimization with incidence relations P Li, O Milenkovic NeurIPS 2018, 2018 | 18 | 2018 |
| Motif and hypergraph correlation clustering P Li, GJ Puleo, O Milenkovic IEEE Transactions on Information Theory 66 (5), 3065-3078, 2019 | 14 | 2019 |
| : Active learning over hypergraphs with pointwise and pairwise queries IE Chien, H Zhou, P Li The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 13 | 2019 |
| F-fade: Frequency factorization for anomaly detection in edge streams YY Chang, P Li, R Sosic, MH Afifi, M Schweighauser, J Leskovec Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 12 | 2021 |
| MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams S Bhatia, A Jain, P Li, R Kumar, B Hooi Proceedings of the Web Conference 2021, 3371-3382, 2021 | 10 | 2021 |
Olgica MilenkovicUniversity of IllinoisVerified email at illinois.edu
