Summary of Learning on Large Graphs Using Intersecting Communities, by Ben Finkelshtein et al.
Learning on Large Graphs using Intersecting Communities
by Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein, Ron Levie
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI); Machine Learning (stat.ML)
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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 This paper proposes a novel approach to alleviate the memory complexity issue in Message Passing Neural Networks (MPNNs) for graph machine learning. MPNNs update each node’s representation by aggregating messages from neighbors, requiring a memory complexity proportional to the number of edges. For large graphs, this can become prohibitive. To address this, the authors propose approximating the input graph as an intersecting community graph (ICG), which allows for efficient graph learning in linear memory and time with respect to the number of nodes. The new pipeline is demonstrated empirically on node classification tasks and spatio-temporal data processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with computers that try to learn from big graphs (like social networks). These computers, called Message Passing Neural Networks, get slow because they have to look at every connection between people. The authors came up with a new way to make this process faster by breaking the graph into smaller groups of friends. This lets the computer do its job much more efficiently and can be used for tasks like predicting what people will like or figuring out where things are in the world. |
Keywords
» Artificial intelligence » Classification » Machine learning
