Mining social ties beyond homophily

Summarizing patterns of connections or social ties in a social network, in terms of attributes information on nodes and edges, holds a key to the understanding of how the actors interact and form relationships. We formalize this problem as mining top-k group relationships (GRs), which captures strong social ties between groups of actors. While existing works focus on patterns that follow from the well known homophily principle, we are interested in social ties that do not follow from homophily, thus, provide new insights. Finding top-k GRs faces new challenges: it requires a novel ranking metric because traditional metrics favor patterns that are expected from the homophily principle; it requires an innovative search strategy since there is no obvious anti-monotonicity for such GRs; it requires a novel data structure to avoid data explosion caused by multidimensional nodes and edges and many-to-many relationships in a social network. We address these issues through presenting an efficient algorithm, GRMiner, for mining top-k GRs and we evaluate its effectiveness and efficiency using real data.