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:
Presenting Author:
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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