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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.
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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
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