Soutenance de thèse Sheng GAO

Bonjour,

J'ai le plaisir de vous inviter à ma soutenance de thèse intitulée «
Latent Factor Models for Link Prediction Problems » le mardi 19 juin, 14
h, au Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et
Marie Curie, Tour 26, couloir 26-00, 1er étage, Salle 105. Vous êtes
cordialement invités ainsi qu'au pot qui suivra.

Comment venir: http://www.lip6.fr/informations/comment.php

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Jury
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Patrick GALLINARI Professeur, Université Pierre et Marie Curie, Paris,
co-directeur de thèse
Ludovic DENOYER Maître de Conférence, Université Pierre et Marie Curie,
co-directeur de thèse
Eric GAUSSIER Professeur, Université Joseph Fourier, Grenoble, rapporteur
François YVON Professeur, Université Paris-Sud, Paris, rapporteur
Mohamed NADIF Professeur, Université Paris Descartes, Paris, examinateur
Fabrice ROSSI Professeur, Université Paris 1 Panthéon-Sorbonne, examinateur


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Title
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Latent Factor Models for Link Prediction Problems


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Abstract
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With the rising of Internet as well as modern social media, relational
data has become ubiquitous, which consists of those kinds of data where
the objects are linked to each other with various relation types.
Accordingly, various relational learning techniques have been studied in a
large variety of applications with relational data, such as recommender
systems, social network analysis, Web mining or bioinformatic. Among a
wide range of tasks encompassed by relational learning, we address the
problem of link prediction in this thesis.

Link prediction has arisen as a fundamental task in relational learning,
which considers to predict the presence or absence of links between
objects in the relational data based on the topological structure of the
network and/or the attributes of objects. However, the complexity and
sparsity of network structure make this a great challenging problem. In
this thesis, we propose solutions to reduce the difficulties in learning
and fit various models into corresponding applications.

Basically, in Chapter 3 we present a unified framework of latent factor
models to address the generic link prediction problem, in which we
specifically discuss various configurations in the models from
computational perspective and probabilistic view. Then, according to the
applications addressed in this dissertation, we propose different latent
factor models for two classes of link prediction problems: (i) structural
link prediction. (ii) temporal link prediction.

In terms of structural link prediction problem, in Chapter 4 we define a
new task called Link Pattern Prediction (LPP) in multi-relational
networks. By introducing a specific latent factor for different relation
types in addition to using latent feature factors to characterize objects,
we develop a computational tensor factorization model, and the
probabilistic version with its Bayesian treatment to reveal the intrinsic
causality of interaction patterns in multi-relational networks. Moreover,
considering the complex structural patterns in relational data, in Chapter
5 we propose a novel model that simultaneously incorporates the effect of
latent feature factors and the impact from the latent cluster structures
in the network, and also develop an optimization transfer algorithm to
facilitate the model learning procedure.

In terms of temporal link prediction problem in time-evolving networks, in
Chapter 6 we propose a unified latent factor model which integrates
multiple information sources in the network, including the global network
structure, the content of objects and the graph proximity information from
the network to capture the time-evolving patterns of links. This joint
model is constructed based on matrix factorization and graph
regularization technique.

Each model proposed in this thesis achieves state-of-the-art performances,
extensive experiments are conducted on real world datasets to demonstrate
their significant improvements over baseline methods. Almost all of them
have been published in international or national peer-reviewed conference
proceedings.

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Cordialement,

Sheng GAO