Pattern Analysis and Intelligent Systems
Zahra narimani; Farhad Soleimanian Gharehchopogh
Articles in Press, Accepted Manuscript, Available Online from 07 July 2022
Abstract
One of the most critical issues in studying complex networks is detecting communities widely used in sociology, social media (such as Instagram, Twitter, or email networks), biology, physics, data networks, and information technology. Graphs usually implement complex network modelling. In a graph, nodes ...
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One of the most critical issues in studying complex networks is detecting communities widely used in sociology, social media (such as Instagram, Twitter, or email networks), biology, physics, data networks, and information technology. Graphs usually implement complex network modelling. In a graph, nodes represent objects, and edges represent connections between objects. Communities are groups of nodes that have many internal connections and few intergroup connections. Although in social network research, the detection of communities has attracted much attention, most of them face functional weaknesses because the structure of the community is not clear or the characteristics of nodes in a community are not the same. Besides, many existing algorithms have complex and costly calculations. In this paper, a model based on Harris Hawk Optimization (HHO) algorithm and Opposition-based Learning (OBL) is proposed for Community Detection (CD). The proposed model uses an OBL to balance exploration and exploitation. The balance between exploration and exploitation is effective in achieving optimal solutions. The evaluation of the proposed model is performed on four different datasets based on modularity criteria and NMI (Normalized Mutual Information). The results show that the proposed model has higher modularity, NMI, and generalizability compared to other methods.
Pattern Analysis and Intelligent Systems
Ehsan Soleimani; Kamal Mirzaie
Articles in Press, Accepted Manuscript, Available Online from 30 July 2022
Abstract
With the increasing use of online services in the form of platforms or websites, as well as the growth of users in online spaces, it seems necessary to study and analyze these networks. One of the areas of interest for social media analysts is Community Detection (CD) in these networks. The purpose of ...
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With the increasing use of online services in the form of platforms or websites, as well as the growth of users in online spaces, it seems necessary to study and analyze these networks. One of the areas of interest for social media analysts is Community Detection (CD) in these networks. The purpose of CD is to discover subcommunities within the original networks. Identifying these groups in social networks has many applications in discovering new groups, and extracting common points between users. The main challenge in CD is to find the relationships between the nodes and form clusters in them. In particular, the use of CD techniques makes it possible to identify similar nodes that have common features. Because user data does not belong to a specific category and there is a lot of data in the community and a similar relationship should be established between them based on the type of data. In this paper, a new method for CD using an Adaptive Genetic Algorithm (AGA) is proposed. The evaluation of the proposed model was performed on the Enron Email suite using Normalized Mutual Information (NMI), Variation Information (VI) and Cross Common Fraction (CCF) criteria. The results show that with increasing the number of generations, the accuracy of the proposed model increases and also the rate of crossover and mutation are very effective in the accuracy of detection. The accuracy of NMI, VI and CCF criteria with 800 iterations is 82.17, 72.95 and 75.16, respectively.
Computer Networks and Distributed Systems
Rahimeh Habibi; Ali Haroun Abadi
Volume 4, Issue 1 , February 2018, , Pages 21-26
Abstract
Today, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex network ...
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Today, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex network and community detection. In modeling the network in terms of a multiplex network, the relationships between users were different in each layer and each user had a rank in each layer. Then, the ratings of two layers including the weight of each layer were aggregated and four effective features of the Trust were achieved. Then, the network was divided into overlapping groups via community detection’ algorithms, each group representative was considered as the community centers and other features were extracted through similar comments. At the end, 48J decision tree algorithm was used to advance the work. The proposed method was assessed on Epinions data set and accuracy of trust was 96%.