Data Mining
Fatemeh Akbari; Eynollah Khanjari
Abstract
Background and Objectives: So far, several methods have been proposed to detect communities, which indicate the high importance of discovering communities for understanding social networks and detecting useful and hidden patterns in the network. The goal of such analyses is to find a group of users with ...
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Background and Objectives: So far, several methods have been proposed to detect communities, which indicate the high importance of discovering communities for understanding social networks and detecting useful and hidden patterns in the network. The goal of such analyses is to find a group of users with common characteristics. Basically, social networks are considered as graphs, so the analysis is also done using graph methods, in which nodes represent individuals and edges represent relationships between them. Since community detection is an NP-complete problem, several meta-heuristic approaches have been used to tackle this problem, mainly considering "modularity" as the objective function. In most approaches, modularity has been used, which suffers from the limitation of resolution and cannot detect communities that are small in size and consider it in combination with large communities.Methods: In this paper, a new hybrid algorithm of bee colony and genetics is proposed for community detection which performs optimization using the "balanced modularity" fitness function. In this algorithm, parallel processing is used to speed up optimization, genetic algorithm is used to create the initial population, and genetic operators are used in the search by bees.Results: Experiments on well-known real-world networks, including karate, American football, dolphins, and political books, have shown that our method provides more accurate results than the state-of-the-art community detection methods.Conclusion: The combined optimization of bee colony and genetics not only provides globally optimal solution but also it does not need prior information about the number as well as the structure of communities.
Social Networks
M. Sabzekar; S. Baradaran Nejad; M. Khazaeipoor
Abstract
Background and Objectives: Nowadays, social networks are recognized as significant sources of information exchange. Consequently, many organizations have chosen social networks as essential tools for marketing and brand management. Communities are essential structures that can enhance the performance ...
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Background and Objectives: Nowadays, social networks are recognized as significant sources of information exchange. Consequently, many organizations have chosen social networks as essential tools for marketing and brand management. Communities are essential structures that can enhance the performance of social networks by grouping nodes and analyzing the information derived from them. This subject becomes more important with the increase in information volume and the complexity of relationships in networks. The goal of community identification is to find subgraphs that are densely connected internally but loosely connected externally.Methods: While community detection has mostly been studied in static networks in the past, this paper focuses on dynamic networks and the influence of central nodes in forming communities. In the proposed algorithm, the network is captured through multiple snapshots. The initial snapshot calculates the influence of each node. Then, by selecting k nodes with higher influence, network communities are formed, and other nodes belong to the community with the most common edges. In the second step, after receiving the next snapshot, communities are updated. Then, k nodes with higher influence are selected, and their associated community is created if needed. If the previous community centers are not among the newly selected k nodes, the community is dissolved, and the nodes within it belong to other communities.Results: Based on the results obtained, the proposed algorithm has managed to achieve better results in most cases compared to the compared algorithms, especially in terms of modularity metrics. The reason behind this success could be attributed to the utilization of influential nodes in community formation.Conclusion: Drawing from the outcomes attained, the suggested algorithm has effectively outperformed the contrasted algorithms in a majority of instances, particularly concerning metrics related to modularity. This accomplishment can potentially be ascribed to the incorporation of influential nodes during the process of community formation.
Modelling, simulation and verification
S.M. Nematollahzadeh; S. Ozgoli; M. Sayad Haghighi
Abstract
Background and Objectives: One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental ...
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Background and Objectives: One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks.Methods: The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are considered. These three models are then proven to satisfy the convexity condition, which means they can be used for the introduced method of identification.Results: Four real experiments have been conducted in this research that their results verify the application of our method. The outcomes of these experiments are presented in this paper.Conclusion: Results show that the provide method is suited for parameter identification for opinion dynamic models.
