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Community Detection in Bipartite Networks

Pourghasemi Najafabadi, Ali | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52388 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Hemmatyar, Ali Mohammad Afshin; Kavousi, Kaveh
  7. Abstract:
  8. In recent decades, a wide range of research has been done on the characteristics of networks in various domains. Community structure is one of the most important properties in networks. However, most community detection methods designed for unipartite networks. Bipartite networks are very important in many different fields because these networks explicitly shows conceptual connections between different types of entities, for example in bioinformatics science, business, social and economic systems. Recently, community detection in bipartite networks has risen attention by the technical community, due to the accessibility of huge bipartite network data from different domains and the spread of novel analytical sample that put them at the core of the research. The aim of this research is to give a two-mode community detection algorithm for bipartite networks. In this thesis, we expand distance dynamics model BiAttractor proposed by Hong-Liang Sun et al. Since the BiAttractor uses Jaccard coefficient in Local Jaccard Distance (LJD) for scoring the links in networks. We are using Adamic-Adar index, called Local Adamic-Adar Distance (LAD), because previous experiments shows that with Adamic-Adar, one can obtain more precise results than Jaccard. Furthermore, our main contribution is the expansion of BiAttractor algorithm to find non-overlapping communities with better performance in terms of modularity and moreover to discover overlapping communities. BiAttractor algorithm detects non-overlapping communities in linear time complexity O(|E|) in sparse networks, where |E| is the number of edges. In addition, we are trying to time complexity to detect overlapping community too linear
  9. Keywords:
  10. Bipartite Networks ; Community Detection ; Bopartite Lable Propagation Algorithm ; New Bipartite Attractor Algorithm

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