An Evaluation of Community Detection Algorithms on Large-Scale Email Traffic
Paper i proceeding, 2012

Community detection algorithms are widely used to study the structural properties of real-world networks. In this paper, we experimentally evaluate the qualitative performance of several community detection algorithms using large-scale email networks. The email networks were generated from real email traffic and contain both legitimate email (ham) and unsolicited email (spam). We compare the quality of the algorithms with respect to a number of structural quality functions and a logical quality measure which assesses the ability of the algorithms to separate ham and spam emails by clustering them into distinct communities. Our study reveals that the algorithms that perform well with respect to structural quality, don’t achieve high logical quality. We also show that the algorithms with similar structural quality also have similar logical quality regardless of their approach to clustering. Finally, we reveal that the algorithm that performs link community detection is more suitable for clustering email networks than the node-based approaches, and it creates more distinct communities of ham and spam edges.

Email networks

Quality functions

Community detection


Farnaz Moradi

Chalmers, Data- och informationsteknik, Nätverk och system

Tomas Olovsson

Chalmers, Data- och informationsteknik, Nätverk och system

Philippas Tsigas

Chalmers, Data- och informationsteknik, Nätverk och system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 7276 LNCS 283-294
9783642308499 (ISBN)


Datavetenskap (datalogi)





Mer information

Senast uppdaterat