Date of Award




Document Type

Master's Thesis

Degree Name

Master of Science (MS)


Department of Computer Science

Content Description

1 online resource (ii, iv, 33 pages) : illustrations (some color)

Dissertation/Thesis Chair

Petko Bogdanov


communities, coordinated, ISI distance, parameter-free, shift-invariant, scalable metric, subgraphs, synchronous, System analysis, Computer networks, Social networks, Communication, Computer algorithms, Coincidence

Subject Categories

Computer Engineering | Computer Sciences


Community detection is a central problem in network analysis. The majority of existing work defines communities as subsets of nodes that are structurally well-connected and isolated from the rest of the network. Apart from their underlying connectivity, nodes in real-world networks exhibit temporal activity: user posts in social networks, browsing activity on web pages and neuron activations in brain networks to name a few. While edges encode potential for community interactions, participation in the community can be quantified by synchronized member activity. Given both the network structure and individual node activity, how can we detect communities that are both well-connected and exhibit synchronized activity?