Inferring structure in bipartite networks using the latent blockmodel and exact ICL

Jason Wyse (1) , Nial Friel (2) , Pierre Latouche (3)

1 School of Computer Science and Statistics [Dublin]
2 UCD - University College Dublin [Dublin]
3 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search approach is vastly more scalable than competing Markov chain Monte Carlo based methods. Application to a number of real observed bipartite networks demonstrate the algorithms discussed.

Mots clés: Bipartite network; network clustering; bi-clustering; blockmodel; model selection; integrated complete likelihood

Domaines: Statistiques [math.ST]

Jason Wyse, Nial Friel, Pierre Latouche. Inferring structure in bipartite networks using the latent blockmodel and exact ICL. Network Science, 2017, 5 (1), pp.45-69. ⟨10.1017/nws.2016.25⟩. ⟨hal-01519780⟩