Summary of Rethinking Node-wise Propagation For Large-scale Graph Learning, by Xunkai Li et al.
Rethinking Node-wise Propagation for Large-scale Graph Learning
by Xunkai Li, Jingyuan Ma, Zhengyu Wu, Daohan Su, Wentao Zhang, Rong-Hua Li, Guoren Wang
First submitted to arxiv on: 9 Feb 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 In this paper, researchers propose a novel approach to scalable graph neural networks (GNNs) that addresses the limitations of current methods. They develop an adaptive topology-aware propagation (ATP) strategy that reduces bias and extracts structural patterns in nodes, improving predictive performance and running efficiency on large-scale web graphs. ATP can be seamlessly integrated into existing GNNs, making it a valuable tool for semi-supervised node classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For big pictures, this paper is about creating better graph neural networks that work well with really big data sets. The researchers want to make sure these networks are fair and don’t get stuck in one way of looking at things. They came up with a new idea called ATP that helps them do just that. It’s like a special tool that can be used with other tools already out there, making it super useful for all kinds of projects. |
Keywords
* Artificial intelligence * Classification * Semi supervised
