Finding redundant and complementary communities in multidimensional networks

Community Discovery in networks is the problem of detecting, for each node, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive. We de ne this problem for multidimensional networks, i.e. where more than one connection may reside between any two nodes. We introduce two measures able to characterize the communities found. Our experiments on real world data support the methodology proposed, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.