Summary of Ags-gnn: Attribute-guided Sampling For Graph Neural Networks, by Siddhartha Shankar Das et al.
AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
by Siddhartha Shankar Das, S M Ferdous, Mahantesh M Halappanavar, Edoardo Serra, Alex Pothen
First submitted to arxiv on: 24 May 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 AGS-GNN is a novel algorithm that addresses the challenge of applying Graph Neural Networks (GNNs) to heterophilic graphs. Traditional GNNs are well-suited for homophilic graphs, where nodes of the same class tend to be connected. However, in heterophilic graphs, where nodes of different classes are more likely to be linked, existing approaches often suffer from high computational costs and lack scalability. AGS-GNN employs a novel sampling paradigm that adapts to both homophily and heterophily by selecting subsets of neighbors based on feature-similarity and feature-diversity. This approach is inductive, allowing for faster convergence and improved test accuracy. The algorithm is also highly parallelizable, making it suitable for large-scale graph processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AGS-GNN is a new way to use Graph Neural Networks (GNNs) on different types of graphs. Right now, GNNs work well with certain types of graphs where similar things are connected together. But when the graph has different kinds of nodes that aren’t usually connected, it’s harder for GNNs to learn from them. AGS-GNN helps by choosing which nodes to look at based on how similar or different they are. This makes it easier and faster for GNNs to learn from these graphs. The best part is that this new way of using GNNs works well even when we only have a small piece of the graph. |
Keywords
» Artificial intelligence » Gnn
