Summary of Dynamic Graph Transformer with Correlated Spatial-temporal Positional Encoding, by Zhe Wang et al.
Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
by Zhe Wang, Sheng Zhou, Jiawei Chen, Zhen Zhang, Binbin Hu, Yan Feng, Chun Chen, Can Wang
First submitted to arxiv on: 24 Jul 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 This paper proposes a novel method for learning effective representations in Continuous-Time Dynamic Graphs (CTDGs). The key challenge is estimating and preserving proximity, which is crucial but challenging due to the sparse and evolving nature of CTDGs. To address this, the authors introduce Correlated Spatial-Temporal Positional encoding, a parameter-free personalized interaction intensity estimation method based on the Poisson Point Process. Building on this, they develop the Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains high-order proximity for effective node representation learning. Experimental results on nine datasets demonstrate the superior performance and scalability of CorDGT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better represent complex networks that change over time, like social media or traffic patterns. The problem is that these networks are hard to analyze because they’re always changing and have many connections between nodes. To solve this, the authors develop a new way to encode spatial-temporal information in these networks. They also create a new model called CorDGT that can learn from these encoded representations. Tests show that their approach works better than others for learning node representations. |
Keywords
» Artificial intelligence » Positional encoding » Representation learning » Transformer
