Summary of Repeat-aware Neighbor Sampling For Dynamic Graph Learning, by Tao Zou et al.
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
by Tao Zou, Yuhao Mao, Junchen Ye, Bowen Du
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 presents a novel approach to dynamic graph learning that incorporates temporal patterns and repeat behavior in evolving data scenarios such as traffic prediction and recommendation systems. The authors argue that traditional methods focus solely on recent neighbors, overlooking the importance of past interactions in predicting future connections. To address this limitation, they introduce RepeatMixer, which considers first- and high-order repeat behavior in neighbor sampling and learns temporal patterns using an MLP-based encoder. The model adapts to varying temporal patterns across different orders through a time-aware aggregation mechanism. Experimental results demonstrate the superiority of RepeatMixer over state-of-the-art models in link prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RepeatMixer is a new way to understand how things change over time. Right now, we’re really good at predicting what will happen next based on recent events. But this method doesn’t work well if we want to predict what will happen after that or even later. That’s because it ignores the fact that some things repeat themselves – like traffic patterns or who your friends are. This new approach looks back in time as well, so it can learn from all those repeated behaviors and make more accurate predictions. |
Keywords
» Artificial intelligence » Encoder
