Summary of Leiden-fusion Partitioning Method For Effective Distributed Training Of Graph Embeddings, by Yuhe Bai et al.
Leiden-Fusion Partitioning Method for Effective Distributed Training of Graph Embeddings
by Yuhe Bai, Camelia Constantin, Hubert Naacke
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Leiden-Fusion partitioning method addresses two key challenges in large-scale training of graph embeddings: minimizing communication overhead and ensuring connected subgraphs. By extending the Leiden community detection algorithm with a greedy merging approach, Leiden-Fusion partitions initially connected graphs into densely connected subgraphs without isolated nodes. This allows for independent GNN training on each partition, reducing network communication and enhancing efficiency. The method is evaluated on multiple benchmark datasets, demonstrating high efficiency while preserving graph embedding quality for node classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make it easier to train computers to understand big networks of data. They want to make sure the computers can handle really large networks without getting stuck or losing information. To do this, they came up with a new way to divide the network into smaller parts that stay connected and don’t get cut off from each other. This makes training faster and more efficient. The scientists tested their method on several big datasets and found it works well. |
Keywords
» Artificial intelligence » Classification » Embedding » Gnn
