Neighborhood discovery via augmented network community structure

Abstract

Abstract The geospatial characteristics of transportation networks structurally constrain their features, and as a result, analysis methods designed for social networks 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 clusters of nodes that align with natural neighborhood extents. 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 improved neighborhood shapes and extents. The use of local network characteristics allows 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.

Keywords

• Transportation networks
• Spatial networks
• Community structure
• Classification
• Machine learning