Predicting Network Centralities from Node Attributes

It’s been a great December, ending the year quite nicely! I attended NIPS, and bumped into my PhD supervisor Yiannis. We had a enjoyable time at the conference and exploring Montreal (a beautiful city). I also presented a poster at the NIPS Workshop on Networks about how to link node features to eigenvector centrality via a probabilistic model; for example, mapping a person’s attributes to how influential he or she is in a social network:

NetworkCentrality_NIPSWsSpotlightAbstract: Among the variety of complex network metrics proposed, node importance or centrality has potentially found the most widespread application—from the identification of gene-disease associations to finding relevant pages in web search. In this workshop paper, we present a method that learns mappings from node attributes to latent centralities. We first construct an eigenvector-based Bayesian centrality model, which casts the problem of computing network centrality as one of probabilistic (latent variable) inference. Then, we develop the sparse variational Bayesian centrality Gaussian process (VBC-GP) which simultaneously infers the centralities and learns the mapping. The VBC-GP possesses inherent benefits: it (i) allows a potentially large number of nodes to be represented by the sparse mapping and (ii) permits prediction of centralities on previously unseen nodes. Experiments show that the VBC-GP learns high-quality mappings and compares favorably to a two-step method, i.e., a full-GP trained on the node attributes and network centralities. Finally, we present a case-study using the VBC-GP to distribute a limited number of vaccines to decrease the severity of a viral outbreak.

Download Paper PDF | Download NIPS Networks Spotlight Slides


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