Summary of Improving Temporal Link Prediction Via Temporal Walk Matrix Projection, by Xiaodong Lu et al.
Improving Temporal Link Prediction via Temporal Walk Matrix Projection
by Xiaodong Lu, Leilei Sun, Tongyu Zhu, Weifeng Lv
First submitted to arxiv on: 5 Oct 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 tackles temporal link prediction, a crucial task for various real-world applications. Previous methods have shown the importance of relative encodings for effective temporal link prediction, but they often lack computational efficiency and only consider structural connectivity, neglecting temporal information. The authors analyze existing relative encodings and unify them as a function of temporal walk matrices, providing a more principled approach to designing relative encodings. They propose TPNet, a temporal graph neural network that incorporates time decay effects and random feature propagation mechanisms to maintain temporal walk matrices, improving computation and storage efficiency. Experimental results on 13 benchmark datasets verify the effectiveness and efficiency of TPNet, outperforming other baselines on most datasets with a maximum speedup of 33.3 compared to the SOTA baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to predict what will happen between people or things in the future based on what happened before. They look at how previous methods work and find that they can be improved by considering both time and connections between things. The authors create a new model called TPNet that is faster and more accurate than other models, and test it on many different datasets to show that it works well. |
Keywords
» Artificial intelligence » Graph neural network
