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Recommendations for the article Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
| Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index |
|---|---|---|---|---|---|---|
| visibility_off | Structure-Aware Mamba: A Linear-Complexity Model for Graph Representation Learning With Global Receptive Fields | Zelong Yang, Shuyan Fang, Yi Cai, Bo Chen, Xiaoping Min | 2026-02-09 | The European Journal on Artificial Intelligence | 0 | 4 |
| visibility_off | HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model | Zhenyu Pan, Yoonsun Jeong, Xiaodan Liu, Han Liu | 2024-05-22 | ArXiv | 5 | 12 |
| visibility_off | Graph Mamba: Towards Learning on Graphs with State Space Models | Ali Behrouz, Farnoosh Hashemi | 2024-02-13 | Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining | 160 | 14 |
| visibility_off | Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space | Xingbo He, Yili Wang, Wenqi Fan, Xu Shen, Xin Juan, Rui Miao, Xin Wang | 2025-01-26 | ArXiv, DBLP | 9 | 7 |
| visibility_off | Learning Long Range Dependencies on Graphs via Random Walks | Dexiong Chen, Till Hendrik Schulz, Karsten M. Borgwardt | 2024-06-05 | ArXiv | 28 | 12 |
| visibility_off | HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling | Thomas Bailie, Yun Sing Koh, S. K. Mukkavilli | 2024-11-18 | Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining | 1 | 6 |
| visibility_off | How Can State Space Models Enhance Machine Learning on Graphs? | Yinan Huang, Siqi Miao, Pan Li | 2026-04-03 | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0 | 7 |
| visibility_off | What Can We Learn from State Space Models for Machine Learning on Graphs? | Yinan Huang, Siqi Miao, Pan Li | 2024-06-09 | ArXiv | 13 | 7 |
| visibility_off | Context Sketching for Memory-efficient Graph Representation Learning | Kai-Lang Yao, Wusuo Li | 2023-12-01 | 2023 IEEE International Conference on Data Mining (ICDM) | 0 | 4 |
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |