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Recently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists. The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems. [ABSTRACT FROM AUTHOR]

Community detection is an important topic for social network analysis and is also essential to understanding complex networks structure. In community detection, the goal is to determine the groups in which the group nodes are densely connected to each other. In this research, deep learning techniques have been used to control graph data with high dimensions, while presenting a comprehensive and integrated architecture of community recognition methods with deep learning. Community detection classic approaches are suitable for networks with low dimensions. Therefore, the reduction of complex network dimensions is counted as a significant topic in community detection. In this paper, in order to reveal the direct and indirect connections among nodes, first a new similarity matrix of network topology is built. Then, a stacked auto-encoder is designed to decrease dimensions based on unsupervised learning. In order to detect communities, various clustering algorithms are then tested and utilized. Evaluation of the proposed research model is performed by surveying various experiments on standard criteria and six real data sets of Karate, Dolphins, Football, Polbooks, Cora and Citeseer. The proposed method evaluation outcomes show a higher accuracy in the identification of communities in the football data set compared to the twelve proposed algorithms used in past researches, and show a significant improvement in other data sets compared to the thirteen algorithms.

In recent years, community detection in social networks has become one of the most important research areas. One of the ways to community detection is to use interactions between users. There are different types of interactions in social networks, which, if used together with network topology, improve the precision of community identification. In this paper, a new method based on the combination of user interactions and network topology is proposed to community detection. In the community formation stage, the effective nodes are identified based on eigenvector centrality, and the primary communities around these nodes are formed based on frequent pattern mining. In the community expansion phase, small communities expand using modularity and the degree of interactions among users. To calculate the degree of interaction between users, a new measure based on the local clustering coefficient and interactions between common neighbors is proposed, which improves the accuracy of the degree of user interactions. Analysis of Higgs Twitter and Flickr datasets utilizing internal density metric, NMI and Omega demonstrates that the proposed method outperforms the other five community detection methods.

Due to the evolution of media technologies in the field of production and distribution of media products and services, user behavior has become an important element in shaping the ecosystem of video watching and downloading websites. In this regard, the current research is focused on the study of the video content consumption behavior of the users and has examined the communication pattern of the members of the ecosystem of Iranian video sites with the technique of social network analysis. The current research was conducted with a quantitative approach and social network analysis method and has 2 research phases: a) sampling of Iranian video download and watch sites through web scraping method, and b) visualization, community detection, and social network analysis of Iranian video watch and download websites. According to the research findings, the ecosystem of video downloading and watching websites in Iran includes 144 elements and 8 communities and includes communities of user-generated content, video on demand, legal purchase, legal reception, illegal reception, subtitles, and East Asian content. The legal downloading community is larger than other communities and is the most important community related to video downloading and watching websites. The present study showed that the audience needs are influential in referring to the video platform and even in moving between the platforms, on the other hand, it can be argued that being an audience has found a searching state and the audience is moving among societies in search of satisfying their needs. According to the active audience theory, it has the ability to influence the media ecosystem through behavior

Recently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists.The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems.

As social networks grow, they become more and more complex and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help the analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. One group of approaches that could solve such problems is the approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize the modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms.

Due to the evolution of media technologies in the field of production and distribution of media products and services, user behavior has become an important element in shaping the ecosystem of video watching and downloading websites. In this regard, the current research is focused on the study of the video content consumption behavior of the users and has examined the communication pattern of the members of the ecosystem of Iranian video sites with the technique of social network analysis. The current research was conducted with a quantitative approach and social network analysis method and has 2 research phases: a) sampling of Iranian video download and watch sites through web scraping method, and b) visualization, community detection, and social network analysis of Iranian video watch and download websites. According to the research findings, the ecosystem of video downloading and watching websites in Iran includes 144 elements and 8 communities and includes communities of user-generated content, video on demand, legal purchase, legal reception, illegal reception, subtitles, and East Asian content. The legal downloading community is larger than other communities and is the most important community related to video downloading and watching websites. The present study showed that the audience needs are influential in referring to the video platform and even in moving between the platforms, on the other hand, it can be argued that being an audience has found a searching state and the audience is moving among societies in search of satisfying their needs. According to the active audience theory, it has the ability to influence the media ecosystem through behavior. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the number of users of social networks has grown significantly. The big challenge for these networks’ audience is How to communicate with the people present on these networks. Friend recommender systems try to fix this challenge by offering suggestions. In this study, data from the social and scientific network of Kousarent were used. In this research, using 10 types of relationships between users without considering the friendship relationships, network graph created, and then by using 3 algorithms Louvain, Kmeans and Hierarchical graph clustering was performed to identify communities. Clusters obtained from Louvain's clustering algorithm had higher percentages of matching with friendships. Then, weights were calculated by genetic algorithm for each of 10 relationships and by applying Louvain clustering algorithm on the network graph, the highest percentage of matching with the optimal weight of each of the 10 relationships was obtained. In this case, the resulting clusters are optimal clusters containing the most similar users. So other users in the same cluster can be suggested as friends. The weight of the edges between the individuals in the graph was also used to prioritize the bids. At the end, the friend proposed method was evaluated and the percentage of suggested friends matched with the individual's true friends was calculated.

Detecting existing communities in social networks is a significant process in analyzing these networks. In recent years, the community detection problem has become popular for detecting structures of social networks. Due to high importance of this problem, various algorithms have been developed in the literature to find communities of complex networks. In this research, a hybrid meta-heuristic consisting of the genetic algorithm (GA) and the invasive weed optimization (IWO) method have been proposed which aims to find appropriate and high quality solutions for the community detection problem. In this hybrid method, the initial solutions are generated via the IWO algorithm, and thereafter the optimization process is continued by means of the genetic algorithm. The proposed algorithm is known as the GAIWO. Fitness of solutions is determined in terms of the modularity density criterion. Modularity density has a maximization essence and determines the quality of detected communities. To evaluate the efficiency of the GAIWO, four other methods have been employed and their results have been compared. Comparisons have been made on several networks with different sizes. Input parameters of all algorithms have been tuned by a design of experiments approach. The outputs indicate appropriate efficiency of the proposed algorithm. Validation of the results have been investigated by means of the Normalized Mutual Information (NMI) metric.

A social network consists of some people who are related to each other through some similarities. The emergence and evolution of these networks and increasing rate of using them is the major cause for social network analysis to be a hot research topic. Using various algorithms, each network can be divided into some communities. So, each community includes some members of the social network. Community detection is one of the most important and fundamental tasks in network analysis. It is a step towards understanding the patterns and characteristics of the complex systems they represent. In this paper, the state of the art algorithms for community detection are categorized into six categories (spectral clustering and centrality, quality function, Label propagation, Structure, Closeness, link clustering) based on their definition of the community and modelling the concept of overlapping (existence of the nodes with membership in multiple communities). Next, these methods are implemented on various datasets and compared to each other. It is obvious from the results of performance measures, even in this small collection of data sets, no algorithm can be considered as the best community detection method for all kinds of networks.

Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies.