Multigraph transformation for community detection applied to financial services

Networks have provided a representation for a wide range of real systems, including communication networks, money transfer networks and biological systems. Communities repre-sent fundamental structures for understanding the organization of real-world networks. Uncovering coherent groups in these networks is the goal of community detection. A community is a mesoscopic structure with nodes heavily connected in their groups by comparison to the nodes in other groups. Commu-nities might also overlap as they may share one or multiple nodes. This paper lays the foundation for an application on transactional multigraphs (networks of financial transactions in which nodes can be linked with multiple edges), through the discovery of communities. Due to their complexity, our goal is to find the most effective way of simplifying multigraphs to weighted graphs, while preserving properties of the network. We tested five weights' calculation function and community detection algorithms were applied. A comparison of the outputs based on extrinsic and intrinsic evaluation metrics is then held.