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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2016

Month Degree Awarded

September

First Advisor

Don Towsley

Subject Categories

Computer Sciences | Statistics and Probability | Theory and Algorithms

Abstract

Networks or graphs are fundamental abstractions that allow us to study many important real systems, such as the Web, social networks and scientific collaboration. It is impossible to completely understand these systems and answer fundamental questions related to them without considering the way their components are connected, i.e., their topology. However, topology is not the only relevant aspect of networks. Nodes often have information associated with them, which can be regarded as node attributes or labels. An important problem is then how to characterize a network w.r.t. topology and node label distributions. Another important problem is how to design efficient algorithms to accomplish tasks on networks. Since nodes often have attributes, an interesting avenue for investigation consists in learning and exploiting existing correlations between node and neighbor attributes for accomplishing a task more efficiently. One of the challenges faced when studying networks in the wild is the fact that in general their topology and information associated with its nodes cannot be directly obtained. Thus, one must resort to collecting the data, but when obtaining the entire network is infeasible, sampling and estimation are the best option. This dissertation investigates the use of sampling and estimation to characterize networks and to accomplish a particular task. More precisely, we study (i) the problem of characterizing directed and undirected networks through random walk-based sampling, (ii) the problem of estimating the set-size distribution from an information-theoretic standpoint, which has application to characterizing the in-degree distribution in large graphs, and (iii) the problem of searching networks to find nodes that exhibit a specific trait while subject to a sampling budget by learning a model from node attributes and structural properties, which has application to recruiting in social networks.

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