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Community Detection in Very Large Networks

Goli, Amir Hossein | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47510 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Beigy, Hamid
  7. Abstract:
  8. Nowadays Systems in different fields of research like computer science, biology, social networks, information networks, and economics are modeled as graphs. The graphs which model real world systems have very different topological characteristics than those of classic networks. One of the prominent characteristics of these networks, is that its not practical to describe a general model for their structure and behavior. As a consequence of this complexity in modeling and structure, these networks are called complex networks. One of the most important observations in complex networks is the presence of communities, it means that in such networks one can separate vertices in disjoint sets, such that vertices in each set are connected by much more edges than vertices from different sets. Such partitioning of complex networks to communities can lead to identification of independent groups of entities in a system, that for example have similar characteristics or similar responsibilities in that system. On the other hand, considering the ever growing size of real complex networks, it’s not hard to see the need for even faster algorithms for community detection. One of the potential solutions for decreasing runtime of these algorithms is to exploit the multi-core and multi-processor architecture of modern computers and design algorithms with parallelism in mind. In this thesis, it’s attempted to propose a parallel approach for fast community detection without sacrificing the qulity of final output partitions. The proposed method was tested on some of selected networks and the results were analyzed accordingly. Results show that the proposed parallel method causes impressive speed-up in detection of communities in tested networks while maintaining the quality of outputs
  9. Keywords:
  10. Complex Network ; Modularity ; Social Networks ; Parallelism ; Community Detection

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