IEEE Transactions on Knowledge and Data Engineering
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Common Neighborhood Sub-graph Density as a Similarity Measure for Community Detection
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Algorithms of the Intelligent Web
Algorithms of the Intelligent Web
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Community detection in Social Media
Data Mining and Knowledge Discovery
An evaluation of community detection algorithms on large-scale email traffic
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Enhancing community detection using a network weighting strategy
Information Sciences: an International Journal
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Smart marketing models could utilize communities within the social Web to target advertisements. However, providing accurate community partitions in a reasonable time is challenging for current online large-scale social networks. In this paper, we propose an approach to enhance community detection in online social networks using node similarity techniques. We apply these techniques on unweighted social networks to detect community structure. Our proposed approach creates a virtual network based on the original social network. Virtual edges are added during this pre-processing step based on nodes' similarity in the original social network. Hence, a virtual link is established between any two similar nodes. Then the landmark CNM algorithm is applied on the generated virtual network to detect communities. This approach, labelled Similarity-CNM is expected to further maximize the quality of the inferred communities in terms of modularity and detection speed. Our experimental evaluation study asserts these gains, which accuracy is supported by a study based on Normalized Mutual Information Measure to determine how similar are the actual communities in the original network and the ones found by the proposed approach in this paper.