Conference Proceedings

Generalized Modularity for Community Detection

Mohadeseh Ganji, Abbas Seifi, Hosein Alizadeh, James Bailey, Peter J Stuckey, A Appice (ed.), PP Rodrigues (ed.), VS Costa (ed.), J Gama (ed.), A Jorge (ed.), C Soares (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER-VERLAG BERLIN | Published : 2015


Detecting the underlying community structure of networks is an important problem in complex network analysis. Modularity is a well-known quality function introduced by Newman, that measures how vertices in a community share more edges than what would be expected in a randomized network. However, this limited view on vertex similarity leads to limits in what can be resolved by modularity. To overcome these limitations, we propose a generalized modularity measure called GM which has a more sophisticated interpretation of vertex similarity. In particular, GM also takes into account the number of longer paths between vertices, compared to what would be expected in a randomized network. We also i..

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