ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Iterative rounding 2-approximation algorithms for minimum-cost vertex connectivity problems
Journal of Computer and System Sciences - Special issue on FOCS 2001
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Online community detection for large complex networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Network community detection--the problem of dividing a network of interest into clusters for intelligent analysis--has recently attracted significant attention in diverse fields of research. To discover intrinsic community structure a quantitative measure called modularity has been widely adopted as an optimization objective. Unfortunately, modularity is inherently NP-hard to optimize and approximate solutions must be sought if tractability is to be ensured. In practice, a spectral relaxationmethod is most often adopted, after which a community partition is recovered from relaxed fractional values by a rounding process. In this paper, we propose an iterative rounding strategy for identifying the partition decisions that is coupled with a fast constrained power method that sequentially achieves tighter spectral relaxations. Extensive evaluation with this coupled relaxation-rounding method demonstrates consistent and sometimes dramatic improvements in the modularity of the communities discovered.