Community detection in complex networks as equilibria

Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Electrical Engineering and Computer Science


Jae C. Oh


Community detection, Social networks, Game theory

Subject Categories

Computer Sciences


The edges of a complex network represent the non-trivial interactions between real-world entities (nodes). Recent mathematical analyses have revealed a great deal about the nature and structure of networks, such as the small-world, scale-free and community structure phenomena. A community structure is a partition of a network into communities based on the network's topology, specifically the set of connections within a community is denser than the set of connections in the entire network. Community structure has been shown to identify functional and logical units of a network's underlying real-world entities. Traditional research in community detection has focused on top-down optimization of global metrics that evaluate the strength of a community structure over an entire network. In contrast, this thesis presents a bottom-up approach based on balancing conflicting local node-metrics, thus achieving an equilibrium. We show through three separate strands of research, that this bottom-up approach can be used to discover important community structures. First we use a force-directed algorithm to cluster nodes in a 2-dimensional plane. Next we use a node association function in conjunction with a team formation algorithm. Our last strand uses solution concepts from game theory to find and analyze stable community structures. We demonstrate the efficacy of our three bottom-up approaches on real-world and benchmark networks. In particular, our game theoretic approach outperforms traditional methods by more than 20% in difficult cases.


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