Summary of Lifelong Graph Learning For Graph Summarization, by Jonatan Frank et al.
Lifelong Graph Learning for Graph Summarization
by Jonatan Frank, Marcel Hoffmann, Nicolas Lell, David Richerby, Ansgar Scherp
First submitted to arxiv on: 25 Jul 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 A novel approach to lifelong graph summarization is proposed, leveraging neural networks to summarize vertices in dynamic web graphs. The method trains networks to summarize graph vertices at a given time, then applies this trained network to subsequent snapshots, retraining and evaluating as needed. GNNs Graph-MLP, GraphSAINT, and an MLP baseline are used for comparison, with 1-hop and 2-hop summaries explored. Extensive experiments on ten weekly web graph snapshots reveal that networks primarily use 1-hop information for summarization, even when performing 2-hop summarization. The approach shows promise in summarizing dynamic web graphs, but also highlights the importance of retraining models to accommodate changes in graph heterogeneity over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand a really big and changing website. This paper talks about how to summarize what’s important on this site at any given time. They use special kinds of computer programs called neural networks to help them do this. The idea is that the program can learn from looking at the site at one point in time, and then apply what it learned to future snapshots of the site. The researchers tested different ways of doing this and found that the program mostly uses information about what’s happening right now to figure out what’s important. They also saw that if they tried to use a program trained on an older snapshot to understand a newer one, it wouldn’t work very well because the website had changed so much over time. |
Keywords
» Artificial intelligence » Summarization
