Summary of Spectral Greedy Coresets For Graph Neural Networks, by Mucong Ding et al.
Spectral Greedy Coresets for Graph Neural Networks
by Mucong Ding, Yinhan He, Jundong Li, Furong Huang
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the limitation of Graph Neural Networks (GNNs) in real-world applications due to their inability to efficiently handle large-scale graphs. To overcome this challenge, the authors propose a novel approach called Spectral Greedy Graph Coreset (SGGC), which selects ego-graphs based on their spectral embeddings. The coreset selection problem is decomposed into two phases: coarse selection of widely spread ego-graphs and refined selection for diversifying topologies. A greedy algorithm is designed to approximately optimize both objectives. The authors demonstrate that SGGC outperforms other coreset methods, generalizes well across GNN architectures, and is much faster than graph condensation. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in machine learning. Imagine you have a huge social network with millions of people, and you want to predict who will be friends with whom. This task is called node classification, but it’s hard because the network is so big. To make it easier, researchers developed a special kind of computer program called Graph Neural Networks (GNNs). However, GNNs are not good at working with huge networks. The authors of this paper came up with a new idea to help GNNs work better with large graphs. They created a way to select the most important parts of the graph and train the GNN on those parts instead of the whole graph. This makes training much faster and more efficient. |
Keywords
» Artificial intelligence » Classification » Gnn » Machine learning