Summary of Graph-skeleton: ~1% Nodes Are Sufficient to Represent Billion-scale Graph, by Linfeng Cao et al.
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
by Linfeng Cao, Haoran Deng, Yang Yang, Chunping Wang, Lei Chen
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper tackles a pressing issue in web graph mining: the scalability of graph models to handle massive amounts of data. The authors argue that by compressing background nodes from large-scale web graphs, they can enable more efficient target node classification. They propose a novel approach, Graph-Skeleton1, which leverages background nodes’ structural connectivity and feature correlation with target nodes. Extensive experiments on various web graph datasets demonstrate the method’s effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Web graphs are everywhere online! But storing and analyzing them is super hard. This paper tries to fix that by finding a way to shrink the extra, unimportant parts of these graphs. They call this “background noise”. The researchers show that these noisy bits help with figuring out important nodes (the targets). Then, they create a new model called Graph-Skeleton1 that helps make it easier and faster to do this target node work. |
Keywords
* Artificial intelligence * Classification
