Integrative Analysis of Dynamic Networks
Doktorsavhandling, 2014
Networks play a central role in several disciplines such as
computational biology, social network analysis, transportation
planning and many others; and consequently, several methods have been developed for network analysis. However, in many cases, the study of a single network is insufficient to discover patterns with multiple facets and subtle
signals. Integrative analysis is necessary in order to fuse weak information
present in multiple networks into a more confident prediction, especially in domains where there are diverse modes of data acquisition e.g.~with modern
biological technologies. This is further complicated by the fact that
most real-world networks are inherently
dynamic in nature. Discerning how networks evolve over time is
crucial to unraveling the underlying phenomenon governing the
system.
Though network science has grown to include advances from diverse fields ranging from
classical results in graph theory and approximation algorithms to
newer methods focussed on study of real-world networks, integrative analysis
of multiple dynamic networks is yet to be fully explored. This
thesis makes two-fold contribution in this area.
The first part of this thesis presents work aimed at integrative analysis of multiple
networks reflecting the diverse relationships
among a common set of actors or nodes. We make the connection between Lovasz theta function, a celebrated result in graph theory, and Kernel methods in
machine learning. This allows us to develop new algorithms for
classical graph-theoretic problems like planted clique recovery, graph
coloring and max k-cut. We also present a new scalable method for discovering
common dense subgraphs from multiple networks, with significant
computational advantage over previous state-of-the-art enumerative
approaches. Motivated by the SVM-theta connection, we design two new
``global'' graph kernels which can be used for graph classification. The kernels
capture global graph properties like girth, while being competitive
with existing ``local'' graph kernels.
The second part of this thesis investigates the problem of learning time-varying interactions
based on node observation data using the framework of probabilistic
graphical models. We explore two facets of this problem: modelling the influence
of gene function on dynamic gene-gene interactions; and, capturing
higher-order time-varying networks in a transport application.
Lovasz theta function
dynamic networks
integrative analysis
probabilistic graphical models
support vector machines
graph kernels
EA-salen, Rännvägen 6B, Chalmers University of Technology
Opponent: Prof. Tony Jebara, Department of Computer Science, Columbia University, USA