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Content Based Community Extraction in Social Networks from Stream Data

Sadegh, Mohammad Mehdi | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41758 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Abolhassani, Hassan
  7. Abstract:
  8. Increasing in social communication via electronic ways has been made social network analysis of these communications more important each day. One of the most important aspects in social network analysis is community detection in such networks. There are many different ways to extract communities from social graph structure which in some of them the content of communication between actors has been noticed in community extraction algorithm. In this thesis after a short survey over advantages and disadvantages of existing methods for community detection, a new method for extracting communities from social networks has been suggested which in addition to streaming property of data it spot the communication topic between actors. In proposed method after subject extraction by data mining methods these subject has been added to social graph as subject nodes and after that resulted graph has been mapped to Euclidean space by means of multidimensional scaling and finally communities and subject has been extracted by distance based clustering algorithm from Euclidean space. Proposed method has been designed based on a Markov chain which in this chain social space structure in each period only depends on its structure in previous period and new data which arrived in current period. Regarding to streaming property of input data in each period instead of storing all communications we only store communities’ position in social space and after generating new communication, communities’ position and structure updated. In each period by only checking new arrived data the communities structure has been updated in short time.

  9. Keywords:
  10. Social Networks ; Community Detection ; Community Extraction ; Topic Modeling ; Stream Social Networks

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