Introduction to algorithms
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
On finding graph clusterings with maximum modularity
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Recommendation of similar users, resources and social networks in a Social Internetworking Scenario
Information Sciences: an International Journal
Modelling collaboration using complex networks
Information Sciences: an International Journal
K-path centrality: a new centrality measure in social networks
Proceedings of the 4th Workshop on Social Network Systems
Information Sciences: an International Journal
A novel measure of edge centrality in social networks
Knowledge-Based Systems
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Maximizing modularity intensity for community partition and evolution
Information Sciences: an International Journal
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
Mixing local and global information for community detection in large networks
Journal of Computer and System Sciences
Information Sciences: an International Journal
An efficient algorithm for community mining with overlap in social networks
Expert Systems with Applications: An International Journal
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A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality, w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph transversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.