1. Learning latent structure in complex networks 1 2
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
Jan Larsen

2. We look very much forward to discuss these results!
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