[Dmbu-l] PhD Defense of Dora Erdos --- April 27@ 10AM in MCS 148

George Kollios gkollios at bu.edu
Wed Apr 22 13:27:20 EDT 2015

*PhD Defense of Dora Erdos*Monday, April 27, 2015 at 10am in MCS 148

*Centrality Measures and Analyzing Dot-product Graphs*

*Abstract:*  In this thesis we investigate two topics in data mining on
graphs; in the first part we investigate the notion of centrality in
graphs, in the second part we look at reconstructing graphs from aggregate
information. In many graph related problems the goal is to rank nodes based
on an importance score. This score is in general referred to as node
centrality. In Part I. we start by giving a novel and more efficient
algorithm for computing betweenness centrality. In many applications not an
individual node but rather a set of nodes is chosen to perform some task.
We generalize the notion of centrality to groups of nodes. While group
centrality was first formally defined by Everett and Borgatti, we are the
first to pose it as a combinatorial optimization problem "find a group of k
nodes with largest centrality". We give an algorithm for solving this
optimization problem for a general notion of centrality that subsumes
various instantiations of centrality that find paths in the graph. We prove
that this problem is NP-hard for specific centrality definitions and we
provide a universal algorithm for this problem that can be modified to
optimize the specific measures. We also investigate the problem of
increasing node centrality by adding or deleting edges in the graph. We
conclude this part by solving the optimization problem for two specific
applications; one for minimizing redundancy in information propagation
networks and one for optimizing the expected number of interceptions of a
group in a random navigational network.

In the second part of the thesis we investigate what we can infer about a
bipartite graph if only some aggregate information -- the number of common
neighbors among each pair of nodes -- is given. First, we observe that the
given data is equivalent to the dot-product of the adjacency vectors of
each node. Based on this knowledge we develop an algorithm that is based on
SVD-decomposition, that is capable of almost perfectly reconstructing
graphs from such neighborhood data. We investigate two versions of this
problem, in the versions the dot-product of nodes with themselves, a.k.a.
the node degrees, are either known or hidden.

PhD Examination Committee:

·         Evimaria Terzi (Major Advisor and 1st Reader)

·         Azer Bestavros (2nd Reader)

·         Pauli Miettinen (3rd Reader, Max Planck Institute for Informatics)

·         Mark Crovella

·         George Kollios (Chair)
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