Covering a Graph with Dense Subgraph Families, via Triangle-Rich Sets

• URL: http://arxiv.org/abs/2407.16850v1
• Date: Tue, 23 Jul 2024 21:26:37 GMT
• Title: Covering a Graph with Dense Subgraph Families, via Triangle-Rich Sets
• Authors: Sabyasachi Basu, Daniel Paul-Pena, Kun Qian, C. Seshadhri, Edward W Huang, Karthik Subbian,
• Abstract summary: Real-world graphs are globally sparse, yet contain numerous "pockets" of high edge density. A fundamental task in graph mining is to discover these dense subgraphs. We give a mathematical formulation of this problem, using a new definition of regularly triangle-rich (RTR) families. We design a provable algorithm, RTRExtractor, that can discover RTR families that approximately cover any RTR set.
• Score: 15.078359519557207
• License:
• Abstract: Graphs are a fundamental data structure used to represent relationships in domains as diverse as the social sciences, bioinformatics, cybersecurity, the Internet, and more. One of the central observations in network science is that real-world graphs are globally sparse, yet contains numerous "pockets" of high edge density. A fundamental task in graph mining is to discover these dense subgraphs. Most common formulations of the problem involve finding a single (or a few) "optimally" dense subsets. But in most real applications, one does not care for the optimality. Instead, we want to find a large collection of dense subsets that covers a significant fraction of the input graph. We give a mathematical formulation of this problem, using a new definition of regularly triangle-rich (RTR) families. These families capture the notion of dense subgraphs that contain many triangles and have degrees comparable to the subgraph size. We design a provable algorithm, RTRExtractor, that can discover RTR families that approximately cover any RTR set. The algorithm is efficient and is inspired by recent results that use triangle counts for community testing and clustering. We show that RTRExtractor has excellent behavior on a large variety of real-world datasets. It is able to process graphs with hundreds of millions of edges within minutes. Across many datasets, RTRExtractor achieves high coverage using high edge density datasets. For example, the output covers a quarter of the vertices with subgraphs of edge density more than (say) $0.5$, for datasets with 10M+ edges. We show an example of how the output of RTRExtractor correlates with meaningful sets of similar vertices in a citation network, demonstrating the utility of RTRExtractor for unsupervised graph discovery tasks.

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