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Lower Ricci Curvature for Efficient Community Detection

Abstract Number:

3802 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yun Jin Park (1), Didong Li (1)

Institutions:

(1) University of North Carolina at Chapel Hill, N/A

Co-Author:

Didong Li  
University of North Carolina at Chapel Hill

First Author:

Yun Jin Park  
University of North Carolina at Chapel Hill

Presenting Author:

Didong Li  
N/A

Abstract Text:

This study introduces the Lower Ricci Curvature (LRC), a novel, scalable, and scale-free discrete curvature designed to enhance community detection in networks. Addressing the computational challenges posed by existing curvature-based methods, LRC offers a streamlined approach with linear computational complexity, making it well-suited for large-scale network analysis. We further develop an LRC-based preprocessing method that effectively augments popular community detection algorithms. Through comprehensive simulations and applications on real-world datasets, including the NCAA football league network, the DBLP collaboration network, the Amazon product co-purchasing network, and the YouTube social network, we demonstrate the efficacy of our method in significantly improving the performance of various community detection algorithms.

Keywords:

Network curvature|Network pruning|Large-scale network| | |

Sponsors:

IMS

Tracks:

Big Data Analytics

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