DTU Informatics / Cognitive Systems

111th December 2009

Learning latent structure in complex networksMorten Mørup and Lars Kai Hansen

Cognitive Systems, DTU Informatics, Denmark

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).

Does latent structure (community detection) modeling assist link prediction compared to heuristic or non-parametric scoring methods?

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How does model flexibility affect identification of latent structure?

211th December 2009

DTU Informatics / Cognitive Systems

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

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):

Community detection approach better than all non-parametric methodsNon-parametric method better than all community detection approaches

Proposed LD model best performing community detection approach