Scalable Subgraph Counting: The Methods Behind The Madness

Date
2019-05-01
Authors
Seshadhri, Comandur
Tirthapura, Srikanta
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Altmetrics
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Research Projects
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Computer Science
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Abstract

Subgraph counting is a fundamental problem in graph analysis that finds use in a wide array of applications. The basic problem is to count or approximate the occurrences of a small subgraph (the pattern) in a large graph (the dataset). Subgraph counting is a computationally challenging problem, and the last few years have seen a rich literature develop around scalable solutions for it. However, these results have thus far appeared as a disconnected set of ideas that are applied separately by different research groups. We observe that there are a few common algorithmic building blocks that most subgraph counting results build on. In this tutorial, we attempt to summarize current methods through distilling these basic algorithmic building blocks. The tutorial will also cover methods for subgraph analysis on “big data” computational models such as the streaming model and models of parallel and distributed computation.

Description

This proceeding is published as Seshadhri, Comandur, and Srikanta Tirthapura. "Scalable Subgraph Counting: The Methods Behind The Madness." In Companion Proceedings of The 2019 World Wide Web Conference (WWW ’19 Companion), May 13– 17, 2019, San Francisco, CA, USA. New York, NY: ACM. (2019): 1317-1318. DOI: 10.1145/3308560.3320092. Posted with permission.

Keywords
subgraph counting, motif counting, graphlet counting, sampling, edge orientation
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