Link Prediction via Ranking with a Multiple Membership Nonparametric Bayesian Model

MPhil Thesis Defence


Title: "Link Prediction via Ranking with
a Multiple Membership Nonparametric Bayesian Model"

By

Mr. Yun-Kwan Chan


Abstract

Link prediction in complex networks has found applications in a wide range 
of real-world domains involving relational data.  The goal is to predict 
some hidden relations between individuals based on the observed relations. 
Existing models are unsatisfactory when more general multiple membership 
in latent groups can be found in the network data.  Taking the 
nonparametric Bayesian approach, we propose a multiple membership latent 
group model for link prediction.  Besides, we argue that existing 
performance evaluation methods for link prediction, which regard it as a 
binary classification problem, do not satisfy the nature of the problem. 
As another contribution of this work, we propose a new evaluation method 
by regarding link prediction as ranking.  Based on this new evaluation 
method, we compare the proposed model with two related state-of-the-art 
models and find that the proposed model can learn more compact structure 
from the network data.


Date:				Monday, 27 August 2012

Time:				2:00pm – 4:00pm

Venue:				Room 3501
 				Lifts 25/26

Committee Members:		Prof. Dit-Yan Yeung (Supervisor)
 				Prof. Nevin Zhang (Chairperson)
 				Dr. Raymond Wong


**** ALL are Welcome ****