Summary of A Survey Of Dynamic Graph Neural Networks, by Yanping Zheng et al.
A survey of dynamic graph neural networks
by Yanping Zheng, Lu Yi, Zhewei Wei
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 comprehensive review, the authors explore dynamic graph neural networks (GNNs) that can capture temporal dependencies in evolving graph-structured data. By incorporating sequence modeling modules into traditional GNN architectures, these models aim to accurately depict complex networks. The paper provides an in-depth look at mainstream dynamic GNN models, categorized by how they incorporate temporal information. Additionally, the authors discuss large-scale models and pre-training techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help us better understand and work with dynamic graphs that change over time. Imagine trying to predict what will happen in a network of people who are connected based on their friendships, and those connections change every day. The authors show how GNNs can be modified to handle this kind of data, which is important for many real-world applications. |
Keywords
» Artificial intelligence » Gnn
