Rapid Betweenness Centrality Estimates for Transportation Networks using Capsule Networks

Measuring importance of nodes in a graph is one of the key aspects in graph analysis. Betweenness centrality (BC) measures the amount of influence that a node has over the flow of information in a graph. However, the computation complexity of calculating BC is extremely high with large-scale graphs. This is especially true when analyzing the road networks with millions of nodes and edges. In this study, we propose a deep learning architecture RoadCaps to estimate BC with sub-second latencies. RoadCaps aggregates features from neighbor nodes using Graph Convolutional Networks and estimates the node level BC by mapping low-level concept to high-level information using Capsule Networks. Our empirical benchmarks demonstrates that RoadCaps outperforms base models such as GCN and GCNFCL in both accuracy and robustness. On average, RoadCaps generates a node’s BC value in 7.5 milliseconds.