List of Articles label propagation algorithm

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  1 - Detection of Communities on Social Networks Based on Label Propagation Algorithm and Fuzzy Methods
  Mohsen Chekin Amin Mehranzadeh
  10.30495/tfss.2022.1954699.1019
  The proliferation of the web and social networks has made people more connected to their friends and neighbors than ever before. The desire of individuals to relate to similar tastes and choices in a social network leads to the formation of clusters or virtual communiti More

  The proliferation of the web and social networks has made people more connected to their friends and neighbors than ever before. The desire of individuals to relate to similar tastes and choices in a social network leads to the formation of clusters or virtual communities. Such information can be useful for commercial, educational or developmental purposes and therefore a large number of algorithms for detecting communities have been presented. There are many algorithms for detecting communities on social networks. In this paper, using the label propagation algorithm and fuzzy Delphi method, an improved method is presented that can identify communities more accurately and quickly than other similar methods. Accordingly, in the proposed algorithm, instead of randomly selecting from the maximum labels of the neighboring nodes, the label with the highest weight is chosen. By doing this, random selection is eliminated, and stability and certainty in the outcomes of the algorithm are achieved. Manuscript profile
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  2 - Using The Gray Wolf Optimization Algorithm for Community Detection
  Maliheh Ghasemzadeh Mohammad Amin  Ghasemzadeh
  10.30486/MJTD.1402.1092331
  In today's world, networks play a very important role in people's lives. One of the important issues related to networks is the issue of detecting communities. These communities are also called groups and clusters. Communities include nodes that are closely related to e More

  In today's world, networks play a very important role in people's lives. One of the important issues related to networks is the issue of detecting communities. These communities are also called groups and clusters. Communities include nodes that are closely related to each other. Most of the nodes that are members of a community have common properties. In social networks, it is important to detect the community in order to analyze the network and it is a very important tool to understand the information of the network and its structure. Studying community detection has garnered significant interest in last few years, leading to the development of numerous algorithms in this area. this research, we used the gray wolf meta-heuristic algorithm and improved it with operators such as mutation, combination, and local search, and also improved the final solution of the gray wolf algorithm with the label propagation algorithm to detect communities. Experiments showed that the proposed method has high accuracy and also due to the applied techniques, the problem converges to the best solution very quickly. Manuscript profile