An Empirical Study of Community Detection Algorithms on Social and Road Networks

Community detection in social networks is a problem with considerable interest, since, discovering

communities reveals hidden information about networks. There exist many algorithms to detect

inherent community structures and recently few of them are investigated on social networks. However,

it is non-trivial to decide the best approach in the presence of diverse nature of graphs, in

terms of density and sparsity, and inadequate analysis of the results. Therefore, in this study, we

analyze and compare various algorithms to detect communities in two networks, namely social

and road networks, with varying structural properties. The algorithms under consideration are

evaluated with unique metrics for internal and external connectivity of communities that includes

internal density, average degree, cut ratio, conductance, normalized cut, and average Jaccard Index.

The evaluation results revealed key insights about selected algorithms and underlying community

structures.

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