| Distgnn: Scalable distributed training for large-scale graph neural networks V Md, S Misra, G Ma, R Mohanty, E Georganas, A Heinecke, D Kalamkar, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 127 | 2021 |
| Deep graph similarity learning: A survey G Ma, NK Ahmed, TL Willke, PS Yu Data Mining and Knowledge Discovery 35, 688-725, 2021 | 102 | 2021 |
| Ddgcn: Dual dynamic graph convolutional networks for rumor detection on social media M Sun, X Zhang, J Zheng, G Ma Proceedings of the AAAI conference on artificial intelligence 36 (4), 4611-4619, 2022 | 75 | 2022 |
| Kernelized support tensor machines L He, CT Lu, G Ma, S Wang, L Shen, SY Philip, AB Ragin International conference on machine learning, 1442-1451, 2017 | 68 | 2017 |
| Robust spammer detection by nash reinforcement learning Y Dou, G Ma, PS Yu, S Xie Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 60 | 2020 |
| Deep graph similarity learning for brain data analysis G Ma, NK Ahmed, TL Willke, D Sengupta, MW Cole, NB Turk-Browne, ... Proceedings of the 28th ACM international conference on information and …, 2019 | 60 | 2019 |
| Multi-view graph embedding with hub detection for brain network analysis G Ma, CT Lu, L He, SY Philip, AB Ragin 2017 IEEE International Conference on Data Mining (ICDM), 967-972, 2017 | 54 | 2017 |
| Multi-view clustering with graph embedding for connectome analysis G Ma, L He, CT Lu, W Shao, PS Yu, AD Leow, AB Ragin Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 51 | 2017 |
| Adversarial attack on hierarchical graph pooling neural networks H Tang, G Ma, Y Chen, L Guo, W Wang, B Zeng, L Zhan arXiv preprint arXiv:2005.11560, 2020 | 38 | 2020 |
| Spatio-temporal tensor analysis for whole-brain fMRI classification G Ma, L He, CT Lu, PS Yu, L Shen, AB Ragin Proceedings of the 2016 siam international conference on data mining, 819-827, 2016 | 34 | 2016 |
| Contrastive brain network learning via hierarchical signed graph pooling model H Tang, G Ma, L Guo, X Fu, H Huang, L Zhan IEEE transactions on neural networks and learning systems, 2022 | 29 | 2022 |
| Commpool: An interpretable graph pooling framework for hierarchical graph representation learning H Tang, G Ma, L He, H Huang, L Zhan Neural Networks 143, 669-677, 2021 | 29 | 2021 |
| Community-preserving graph convolutions for structural and functional joint embedding of brain networks J Liu, G Ma, F Jiang, CT Lu, SY Philip, AB Ragin 2019 IEEE International Conference on Big Data (Big Data), 1163-1168, 2019 | 28 | 2019 |
| Multi-graph clustering based on interior-node topology with applications to brain networks G Ma, L He, B Cao, J Zhang, PS Yu, AB Ragin Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 23 | 2016 |
| Line graph contrastive learning for link prediction Z Zhang, S Sun, G Ma, C Zhong Pattern Recognition 140, 109537, 2023 | 20 | 2023 |
| A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images H Jia, H Tang, G Ma, W Cai, H Huang, L Zhan, Y Xia Computers in biology and medicine 155, 106698, 2023 | 20 | 2023 |
| Explainable survival analysis with convolution-involved vision transformer Y Shen, L Liu, Z Tang, Z Chen, G Ma, J Dong, X Zhang, L Yang, Q Zheng Proceedings of the AAAI Conference on Artificial Intelligence 36 (2), 2207-2215, 2022 | 14 | 2022 |
| A distributed graph-theoretic framework for automatic parallelization in multi-core systems G Ma, Y Xiao, T Willke, N Ahmed, S Nazarian, P Bogdan Proceedings of Machine Learning and Systems 3, 550-568, 2021 | 12 | 2021 |
| Similarity learning with higher-order graph convolutions for brain network analysis G Ma, NK Ahmed, T Willke, D Sengupta, MW Cole, NB Turk-Browne, ... arXiv preprint arXiv:1811.02662, 2018 | 12 | 2018 |
| A comprehensive survey of complex brain network representation H Tang, G Ma, Y Zhang, K Ye, L Guo, G Liu, Q Huang, Y Wang, O Ajilore, ... Meta-Radiology, 100046, 2023 | 11 | 2023 |
