Summary of Dg-mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models, by Haonan Yuan et al.
DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
by Haonan Yuan, Qingyun Sun, Zhaonan Wang, Xingcheng Fu, Cheng Ji, Yongjian Wang, Bo Jin, Jianxin Li
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DG-Mamba framework is a novel Dynamic Graph structure learning approach that tackles the challenges of poor robustness in Dynamic Graph Neural Networks (DGNNs). By leveraging Selective State Space Models (Mamba) and kernelized dynamic message-passing, DG-Mamba reduces quadratic complexity to linear, enabling efficient spatio-temporal structure learning. The framework also captures global intrinsic dynamics by discretizing system states with cross-snapshot graph adjacency and utilizing selective snapshot scan for long-distance dependencies. Furthermore, self-supervised Principle of Relevant Information for DGSL regularizes the most relevant yet least redundant information, enhancing global robustness. Experimental results demonstrate the superiority of DG-Mamba in terms of robustness and efficiency compared to state-of-the-art baselines against adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dynamic Graphs are everywhere! They help us understand how things change over time and space. But current models can be really bad at dealing with noise, missing information, and other problems that make it hard for them to learn from the data. The new DG-Mamba framework is a way to improve these models by finding the best structure for the graph. It uses some clever tricks like reducing complexity and capturing long-term patterns to make sure the model works well even when things get messy. |
Keywords
» Artificial intelligence » Self supervised
