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Community Detection on Edge-labeled Graphs using Spark

How can we detect communities in graphs with edge-labels, such as time-evolving networks or edge-colored graphs? Unlike classical graphs, edge-labels contain additional information about the type of edges, e.g., when two people got connected, or which company hosts the air route between two cities. We model community detection on edge-labeled graphs as a tensor decomposition problem and propose TeraCom, a distributed system that is able to scale in order to solve this problem on 10x larger graphs. By carefully designing our algorithm and leveraging the Spark framework, we show how to achieve better accuracy (in terms of recovering ground-truth communities) when compared to PARAFAC methods up to 30% increase in NMI. We also present interesting clusters discovered by our system in a flights network.