  Membership diversity is a characteristic aspect of social networks in which a
person may belong to more than one social group. For this reason, discovering
overlapping structures is necessary for realistic social analysis. In this
paper, we present a fast algorithm1, called SLPA, for overlapping community
detection in large-scale networks. SLPA spreads labels according to dynamic
interaction rules. It can be applied to both unipartite and bipartite networks.
It is also able to uncover overlapping nested hierarchy. The time complexity of
SLPA scales linearly with the number of edges in the network. Experiments in
both synthetic and real- world networks show that SLPA has an excellent
performance in identifying both node and community level overlapping
structures.
