An evaluation of community detection algorithms on large-scale email traffic

Authors:
Farnaz Moradi;Tomas Olovsson;Philippas Tsigas
Affiliations:
Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden;Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden;Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden
Venue:
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Year:
2012

Citing 6
Cited 1

On clusterings-good, bad and spectral

FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Empirical comparison of algorithms for network community detection

Proceedings of the 19th international conference on World wide web
An analysis of social network-based Sybil defenses

Proceedings of the ACM SIGCOMM 2010 conference
On collection of large-scale multi-purpose datasets on internet backbone links

Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security
Is there a best quality metric for graph clusters?

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Survey: Graph clustering

Computer Science Review

Community detection in social networks through similarity virtual networks

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.