Date of Award

1-1-2016

Language

English

Document Type

Master's Thesis

Degree Name

Master of Science (MS)

College/School/Department

Department of Computer Science

Content Description

1 online resource (iv, 15 pages) : illustrations (some color)

Dissertation/Thesis Chair

Petko Bogdanov

Keywords

Community Detection, Network Mining, Pruning, Spectral Clustering, Temporal Graph, Computer networks, Communication, Social networks, Computer algorithms

Subject Categories

Computer Sciences

Abstract

Local community detection is an important tool for the analysis of networks of different genres. The goal is to identify only the best communities in a network instance as opposed to computing a partitioning of the whole network. The majority of the work on local community detection has focused on static networks with less attention on networks that evolve over time. Given a trace of temporal interaction among nodes in a network, how can we detect a period of high interaction for a specific group of nodes? To help temporal community detection with the need to search in the time domain in addition to the graph structure a time period based pruning solution is presented.

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