Neighborhood Discovery via Network Community Structure

Abstract

Compared to social and information networks, the geospatial characteristics of transportation networks make them structurally constrained. Although road, flight, train, and other such networks have been analyzed using social network analysis methods, the results typically fail to capture useful characteristics or make informative comparisons. In the case of road networks, natural constraints on the edge distribution weaken the ability of standard community detection algorithms to find intuitively separable neighborhoods. We show that by adding edge weights based on the similarity of localized subgraph features we can apply modularity-based community detection algorithms to uncover intuitively distinct neighborhoods. The use of local network characteristics allow the feature analysis to be completed in linear time, thus making the approach expandable to very large networks. We demonstrate this technique with an application to central Tokyo.