Summary of State Space Models on Temporal Graphs: a First-principles Study, by Jintang Li et al.
State Space Models on Temporal Graphs: A First-Principles Study
by Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates deep learning techniques for modeling complex systems that exhibit dynamic behaviors, formalized as temporal graphs. Temporal graphs are sequences of static graph snapshots observed at discrete time points. Existing models, such as RNNs and Transformers, struggle with long-range dependencies or computational complexity. State space models (SSMs) have shown promise in independent sequence modeling, but this paper extends SSM theory to incorporate structural information into temporal graph modeling via Laplacian regularization. The authors develop GraphSSM, a graph state space model for dynamic graph modeling. Experimental results demonstrate the effectiveness of GraphSSM across various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how computers can learn from complex systems that change over time. These systems are like snapshots of a graph taken at different times. Current computer models struggle to understand these changing patterns, so this paper develops new techniques called state space models to help with this task. The authors test their new model, GraphSSM, on various data sets and find it works well. |
Keywords
» Artificial intelligence » Deep learning » Regularization
