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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

First Day at SMART.

SMART Desk viewWell, my first working day at SMART is almost over. 4 minutes and 35 seconds to the stipulated end-of-work-day. But who’s counting? So far, it’s been interesting — met the friendly folks here and saw the cool toys (autonomous vehicles). I’m one of the “early birds” and managed to land a desk with a great view of the NUS campus. That said, I might move down to “The Garage” where all the robots/machines are. Hopefully, I’ll sort out all my administration stuff soon and get on to playing working with the vehicles and some new learning methods I have in mind.

Rebuilding libstdcxx using macports on Mountain Lion

I did the unthinkable and upgraded my OS (in my final year of my PhD!). And surprise-surprise, some of my code wouldn’t compile anymore. I figured I needed to rebuild my macports-installed *nix software but ran into problems with gcc45 and libstdcxx. The issue is a ld64 bug, that was fixed using user adrian’s solution (replicated here):

sudo port uninstall ld64
sudo port -v install ld64
sudo port clean libstdcxx
sudo port -d build libstdcxx build.jobs=1
sudo port install libstdcxx

 

Deadline over…

A little exhausted after a couple of paper submissions to IROS. Looking forward to a break for a few days while I re-organise and re-think. And some opportunity for reading (Kantz and Schreiber’s Nonlinear Time Series Analysis that I bought ages ago but haven’t finished, and Chaitin’s Meta Math that I have read but have largely forgotten). 

Plus, my desk is clean again! 

 

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