Summary of Dygmamba: Efficiently Modeling Long-term Temporal Dependency on Continuous-time Dynamic Graphs with State Space Models, by Zifeng Ding et al.
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
by Zifeng Ding, Yifeng Li, Yuan He, Antonio Norelli, Jingcheng Wu, Volker Tresp, Yunpu Ma, Michael Bronstein
First submitted to arxiv on: 8 Aug 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 The proposed CTDG representation learning model, DyGMamba, addresses the challenges of learning useful representations for continuous-time dynamic graphs (CTDGs). The model leverages two node-level and time-level state space models (SSMs) to encode historical node interactions and exploit temporal patterns. This approach enables the selection of critical information from interaction histories, achieving low computational complexity while maintaining high performance in tasks like dynamic link prediction. Experimental results demonstrate DyGMamba’s state-of-the-art performance on most cases, making it a suitable choice for applications requiring efficient representation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DyGMamba is a new way to understand and work with complex networks that change over time. Right now, it’s hard to teach computers how to learn from these networks because they need to remember lots of information about what happened in the past. To fix this problem, researchers created DyGMamba, which uses two special models called state space models. These models help computers identify the most important information from the past and use that to make better predictions about what will happen next. In tests, DyGMamba did better than other methods at predicting links between nodes in these networks. |
Keywords
* Artificial intelligence * Representation learning
