Speaker
David Saad
(Aston University Birmingham)
Description
Not all nodes in a network are created equal. Differences
and similarities exist at both individual node and group
levels. Disentangling single node from group properties is
crucial for network modelling and structural inference.
Based on unbiased generative probabilistic exponential
random graph models and employing distributive message
passing techniques, we present an efficient algorithm that
allows one to separate the contributions of individual nodes
and groups of nodes to the network structure. This leads to
improved detection accuracy of latent class structure in real
world data sets compared to models that focus on group
structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the
statistical significance of small subgraph (motif) distributions
in networks may be sufficient to explain most of the
observed statistics. We show the predictive power of such
generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man
(OMIM) database. The approach is suitable for both directed
and undirected uni-partite as well as for bipartite networks.
Reichardt J, Alamino R, Saad D (2011) The Interplay
between Microscopic and Mesoscopic Structures in Complex
Networks. PLoS ONE 6(8): e21282.
doi:10.1371/journal.pone.0021282
