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Activity Number: 95 - Network Data Analysis
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #322849
Title: Subsampling Based Community Detection for Large Networks
Author(s): Yuguo Chen*
Companies: University of Illinois at Urbana-Champaign
Keywords: Community detection; Computational efficiency; Subsampling

Large networks are increasingly prevalent in many scientific applications. Statistical analysis of such large networks become prohibitive due to exorbitant computation cost and high memory requirements. We develop a subsampling based divide-and-conquer algorithm for community detection in large networks. This method saves both memory and computation costs significantly as one needs to store and process only the smaller subnetworks. This method is also parallelizable which makes it even faster. We derive theoretical upper bounds for the error rate of the algorithm applied with community detection algorithms. We demonstrate the effectiveness of the algorithm on simulated and real networks.

Authors who are presenting talks have a * after their name.

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