A Framework for Constrained Graph Partitioning

Abstract

Social networks offer services such as recommendations of social events, or delivery of targeted advertising material to certain users. In my thesis, I focus on a specific type of services modeled as constrained graph partitioning (CGP). CGP assigns nodes of a graph to a set of classes with bounded capacities so that the similarity and the social costs are minimized. The similarity cost is proportional to the dis-similarity between a node and its class, whereas the social cost is measured in terms of neighbors that are assigned to different classes. I investigate two solutions for CGP: the first utilizes a game-theoretic framework, while the second employs local search. I show that the two approaches can be unified under a common framework, and develop a number of optimization techniques to improve result quality and facilitate efficiency. Experiments with real datasets demonstrate that the proposed methods outperform the state-of-the art in terms of solution quality, while they are significantly faster.