Communities and beyond: Mesoscopic analysis of a large social network with complementary methods

Gergely Tibély*, Lauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész, Jari Saramäki

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.

Original languageEnglish
Article number056125
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume83
Issue number5
DOIs
StatePublished - 31 May 2011
Externally publishedYes

Fingerprint

Dive into the research topics of 'Communities and beyond: Mesoscopic analysis of a large social network with complementary methods'. Together they form a unique fingerprint.

Cite this