List of Articles Centrality Measure

• Open Access Article
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  1 - Friendship Selection Based on Social Features in Social Internet of Things
  Mohammad Mahdian S.Mojtaba Matinkhah
  The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right More

  The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms. Manuscript profile
• Open Access Article
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  2 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
  Ali Khosrozadeh Ali Movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More

  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
  Ali Khosrozadeh movaghar movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More

  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile