  Social networks often encode community structure using multiple distinct
types of links between nodes. In this paper we introduce a novel method to
extract information from such multi-layer networks, where each type of link
forms its own layer. Using the concept of Pareto optimality, community
detection in this multi-layer setting is formulated as a multiple criterion
optimization problem. We propose an algorithm for finding an approximate Pareto
frontier containing a family of solutions. The power of this approach is
demonstrated on a Twitter dataset, where the nodes are hashtags and the layers
correspond to (1) behavioral edges connecting pairs of hashtags whose temporal
profiles are similar and (2) relational edges connecting pairs of hashtags that
appear in the same tweets.
