| False information on web and social media: A survey S Kumar, N Shah arXiv preprint arXiv:1804.08559, 2018 | 527 | 2018 |
| Data Augmentation for Graph Neural Networks T Zhao, Y Liu, L Neves, O Woodford, M Jiang, N Shah AAAI, 2021 | 408 | 2021 |
| FRAUDAR: Bounding Graph Fraud in the Face of Camouflage B Hooi, HA Song, A Beutel, N Shah, K Shin, C Faloutsos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 392 | 2016 |
| Is Homophily a Necessity for Graph Neural Networks? Y Ma, X Liu, N Shah, J Tang ICLR, 2022 | 261 | 2022 |
| Compressing the incompressible with ISABELA: In-situ reduction of spatio-temporal data S Lakshminarasimhan, N Shah, S Ethier, S Klasky, R Latham, R Ross, ... Euro-Par 2011 Parallel Processing: 17th International Conference, Euro-Par …, 2011 | 246 | 2011 |
| TimeCrunch: Interpretable Dynamic Graph Summarization N Shah, D Koutra, T Zou, B Gallagher, C Faloutsos Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 182 | 2015 |
| Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation S Zhang, Y Liu, Y Sun, N Shah ICLR, 2022 | 174 | 2022 |
| DeltaCon: Principled Massive-Graph Similarity Function with Attribution D Koutra, N Shah, JT Vogelstein, B Gallagher, C Faloutsos ACM Transactions on Knowledge Discovery from Data (TKDD) 10 (3), 28, 2016 | 165 | 2016 |
| A unified view on graph neural networks as graph signal denoising Y Ma, X Liu, T Zhao, Y Liu, J Tang, N Shah Proceedings of the 30th ACM International Conference on Information …, 2021 | 163 | 2021 |
| Birdnest: Bayesian inference for ratings-fraud detection B Hooi, N Shah, A Beutel, S Günnemann, L Akoglu, M Kumar, D Makhija, ... Proceedings of the 2016 SIAM International Conference on Data Mining, 495-503, 2016 | 157 | 2016 |
| From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness L Zhao, W Jin, L Akoglu, N Shah ICLR, 2022 | 152 | 2022 |
| Spotting suspicious link behavior with fbox: An adversarial perspective N Shah, A Beutel, B Gallagher, C Faloutsos 2014 IEEE International Conference on Data Mining, 959-964, 2014 | 132 | 2014 |
| Graph Condensation for Graph Neural Networks W Jin, L Zhao, S Zhang, Y Liu, J Tang, N Shah ICLR, 2022 | 131 | 2022 |
| Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings GB Guacho, S Abdali, N Shah, EE Papalexakis 2018 IEEE/ACM International Conference on Advances in Social Networks …, 2018 | 126 | 2018 |
| ISABELA for effective in situ compression of scientific data S Lakshminarasimhan, N Shah, S Ethier, SH Ku, CS Chang, S Klasky, ... Concurrency and Computation: Practice and Experience 25 (4), 524-540, 2013 | 115 | 2013 |
| Graph data augmentation for graph machine learning: A survey T Zhao, W Jin, Y Liu, Y Wang, G Liu, S Günnemann, N Shah, M Jiang arXiv preprint arXiv:2202.08871, 2022 | 102 | 2022 |
| ISOBAR preconditioner for effective and high-throughput lossless data compression ER Schendel, Y Jin, N Shah, J Chen, CS Chang, SH Ku, S Ethier, ... 2012 IEEE 28th international conference on data engineering, 138-149, 2012 | 87 | 2012 |
| Graph-based fraud detection in the face of camouflage B Hooi, K Shin, HA Song, A Beutel, N Shah, C Faloutsos ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (4), 1-26, 2017 | 86 | 2017 |
| Edgecentric: Anomaly detection in edge-attributed networks N Shah, A Beutel, B Hooi, L Akoglu, S Gunnemann, D Makhija, M Kumar, ... 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016 | 86 | 2016 |
| Automated Self-Supervised Learning for Graphs W Jin, X Liu, X Zhao, Y Ma, N Shah, J Tang ICLR, 2022 | 78 | 2022 |
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