List of Articles community detection

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  1 - Introducing a genetic algorithm based Method for Community person's stance Detection in social media and news
  mehdi salkhordeh haghighi Seyyed Mohammad  ebrahimi
  20.1001.1.27170411.1398.11.41.6.1
  News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the r More

  News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the readers to obtain a basic knowledge about the subject and the purpose of various documents. In the present paper, we introduce a method for detecting communities that includes the persons with the same stances and ideas. To do this, the persons referenced in different documents are clustered into communities that have related positions and stances. In the presented method. Community-based personalities are identified based on a friendship network as a base method. Then by using a genetic algorithm, the way that these communities are identified is improved. The criterion in the tests is rand index of detection of these communities. The experiments are designed based on real databases that published in Google News on a particular topic. The results indicate the efficiency and desirability of the proposed method Manuscript profile
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  2 - An Improved Method Based on Label Propagation and Greedy Approaches for Community Detection in Dynamic Social Networks
  Mohammad ستاری kamran zamanifar
  20.1001.1.27170414.1399.12.43.1.2
  Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is More

  Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is simple and fast approach. This approach consists of many methods such as LabelRankT is one with high speed and less complexity. Similar to most methods for detecting communities in dynamic social networks, this one is not trouble free. That is, it is not considered the internal connection of communities, when it expands communities of the previous snapshots in the current snapshot. This drawback decreases the accuracy of community detection in dynamic social networks. For solving the drawback, a greedy approach based on local modularity optimization is added to LabelRankT method. Here, the newly proposed GreedyLabelRankT, LabelRankT and non-overlapping version of Dominant Label Propagation Algorithm Evolutionary (DLPAE-Non Overlapping) on real and synthetic datasets are implemented. Experimental results on both real and synthetic network show that the proposed method detect communities more accurately compared to the benchmark methods. Moreover, the finding here show that running time of the proposed method is close to LabelRankT. Therefore, the proposed method increase the accuracy of community detection in dynamic social networks with no noticeable change in the running time of that. Manuscript profile
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  3 - A comprehensive survey on the influence maximization problem in social networks
  mohsen taherinia mahdi Esmaeili Behrooz Minaei
  20.1001.1.27170411.1401.14.53.16.6
  With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency a More

  With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches Manuscript profile
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  4 - 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
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  5 - 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