Learning latent structure in complex networks Morten Mørup and Lars Kai Hansen

1. Learning latent structure in complex networks Morten Mørup and Lars Kai Hansen Cognitive Systems, DTU Informatics, Denmark How does model flexibility affect identification of latent structure? 1 Does latent structure (community detection) modeling assist link prediction compared to heuristic or non-parametric scoring methods? 2 To take degree distribution into account in the latent modeling we propose the Link Density model (LD) - an extension of the Mixed Membership Stochastic Block Model (Airoldi et al, 2008). 11th December 2009

2. Most community detection approaches can be posed as a standard continuous optimization problem of what we define as the generalized Hamiltonian for graph clustering (GHGC): We evaluated a variety of community detection approaches and non-parametric methods in terms of their ability to predict links (AUC score) on 3 synthetic and 11 benchmark complex networks Community detection approach better than all non-parametric methods Non-parametric method better than all community detection approaches Proposed LD model best performing community detection approach We look very much forward to discuss these results! 11th December 2009