October 18, 2016 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistical Machine Learning Seminar##\\ Hosted by Department of Mathematical Sciences</WRAP>

~~META:title=October 18, 2016~~
  * Date: Tuesday, October 18, 2016
  * Time: 12:00-1:00
  * Room: WH-100E
  * Speaker: Yang Feng (Columbia University)
  * Title: Community detection with nodal information

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<WRAP centeralign>**//Abstract//**</WRAP>
Discovering community structure is one of the fundamental
issues in the study of networked data. Most existing community
detection approaches take merely edge information as inputs, and
deliver suboptimal results for networks with nodal covariates
available. Regarding those networks,  it is desirable to leverage
covariates information for the improvement of detection accuracy.
Towards this goal, we propose a flexible network model incorporating
nodal signals, and develop likelihood-based inference methods. We will
present a systematic study from both theoretical and practical
aspects. Our theoretical analysis demonstrates favorable asymptotic
properties of the proposed approach. We then derive practical
algorithms for the search of the theoretical estimators. Numerical
experiments show the effectiveness of our method in utilizing nodal
information across a variety of simulated and real networked datasets.

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