Peer influence groups: identifying dense clusters in large networks

Sociologists have seen a dramatic increase in the size and availability of social network data. This represents a poverty of riches, however, since many of our analysis techniques cannot handle the resulting large (tens to hundreds of thousands of nodes) networks. In this paper, I provide a method for identifying dense regions within large networks based on a peer influence model. Using software familiar to most sociologists, the method reduces the network to a set of m position variables that can then be used in fast cluster analysis programs. The method is tested against simulated networks with a known small-world structure showing that the underlying clusters can be accurately recovered. I then compare the performance of the procedure with other subgroup detection algorithms on the MacRea and Gagnon prison friendship data and a larger adolescent friendship network, showing that the algorithm replicates other procedures for small networks and outperforms them on the larger friendship network. © 2001 Elsevier Science B.V. All rights reserved.