Summary of Graph Sparsification Via Mixture Of Graphs, by Guibin Zhang et al.
Graph Sparsification via Mixture of Graphs
by Guibin Zhang, Xiangguo Sun, Yanwei Yue, Chonghe Jiang, Kun Wang, Tianlong Chen, Shirui Pan
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Mixture-of-Graphs (MoG) framework addresses the computational challenges faced by Graph Neural Networks (GNNs) when applied to large-scale graphs. MoG leverages the concept of Mixture-of-Experts (MoE) to dynamically select tailored pruning solutions for each node, thereby mitigating the need for a single global sparsity setting and uniform pruning criteria. This local approach leads to optimized sparse graphs that outperform dense graphs on four OGB datasets and two superpixel datasets using five GNN backbones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are powerful tools for processing graph-structured data, but they can be slow when dealing with large graphs. To speed things up, researchers have developed methods to remove unimportant edges from the graph. However, these methods often rely on a single approach that doesn’t take into account the unique characteristics of each node in the graph. The new Mixture-of-Graphs (MoG) method addresses this issue by selecting the best pruning strategy for each node individually. This leads to faster and more efficient GNN processing without sacrificing accuracy. |
Keywords
» Artificial intelligence » Gnn » Mixture of experts » Pruning
