Graph-Skeleton: Less than 2% Nodes are Sufficient to Represent Billion-Scale Graph

Linfeng Cao, Haoran Deng, Yang Yang, Chunping Wang, Lei CHEN

Keywords: Graph Mining, Data Compression, Large-scale Web Graph, Graph Neural Networks

Abstract:

Due to the ubiquity of graph data on the web, web graph mining has become a hot research spot. Nonetheless, the prevalence of large-
scale web graphs in real applications poses significant challenges to storage, computational capacity and graph model design. Despite
numerous studies to enhance the scalability of graph models, a noticeable gap remains between academic research and the practical
web graph mining applications. One major cause is that in most industrial scenarios, only a small part of nodes in a web graph are actually required to be analyzed, where we term these nodes as target nodes, while others as background nodes. In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle
the obstacles fundamentally. To this end, we make the first attempt to study the problem of massive background nodes compression
for target nodes classification. Through extensive experiments, we reveal two critical roles played by the background nodes in target node classification: enhancing structural connectivity between target nodes, and feature correlation with target nodes. Following this, we propose a novel Graph-Skeleton model, which properly fetches the background nodes, and further condenses the semantic and topological information of background nodes within similar target-background local structures. Extensive experiments on various web graph datasets demonstrate the effectiveness and efficiency of the proposed method. In particular, for MAG240M dataset with 0.24 billion nodes, our generated skeleton graph achieves highly comparable performance while only containing 1.8% nodes of the original graph.

Track: Graph Algorithms and Learning for the Web

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Submission Number: 761