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        1 - Scalable Community Detection through Content and Link Analysis in Social Networks
        Zahra  Arefian Mohammad Reza  Khayyam Bashi
        Social network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. Full Text
        Social network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. Using features that are available in the social networks, first discovering a complete and comprehensive communication should be done. Many methods have been proposed to explore the community, which are community detections through link analysis and nodes content. Most of the research exploring the social communication network only focuses on the one method, while attention to only one of the methods would be a confusion and incomplete exploration. Community detections is generally associated with graph clustering, most clustering methods rely on analyzing links, and no attention to regarding the content that improves the clustering quality. In this paper, to scalable community detections, an integral algorithm is proposed to cluster graphs according to link structure and nodes content, and it aims finding clusters in the groups with similar features. To implement the Integral Algorithm, first a graph is weighted by the algorithm according to the node content, and then network graph is analyzed using Markov Clustering Algorithm, in other word, strong relationships are distinguished from weak ones. Markov Clustering Algorithm is proposed as a Multi-Level one to be scalable. The proposed Integral Algorithm was tested on real datasets, and the effectiveness of the proposed method is evaluated. Manuscript Document
      • Open Access Article

        2 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
        Hamideh Sadat Cheraghchi Ali Zakerolhossieni
        The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social Full Text
        The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript Document
      • Open Access Article

        3 - Analysis of expert finding algorithms in social network in order to rank the top algorithms
        AhmadAgha kardan بهنام بزرگی
        The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed f Full Text
        The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed for knowledge-sharing aim to improve user knowledge, making it imperative to have a mechanism that can evaluate users’ knowledge level or in other words “to find experts”. There is a need for expert-finding algorithms in social networks or any other knowledge sharing environment like question and answer communities. There are various content analysis and link analysis methods for expert-finding in social networks. This paper aims to challenge four algorithms by applying them to our dataset and analyze the results in order to compare the algorithms. The algorithms suitable for expert finding has been found and ranked. Based on the results and tests it is concluded that the Z-score algorithm has a better performance than others. Manuscript Document
      • Open Access Article

        4 - Node Classification in Social Network by Distributed Learning Automata
        Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri Kadkhoda
        The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitio Full Text
        The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved. Manuscript Document
      • Open Access Article

        5 - A Hybrid Cuckoo Search for Direct Blockmodeling
        Saeed NasehiMoghaddam mehdi ghazanfari babak teimourpour
        As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within posit Full Text
        As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field. Manuscript Document
      • Open Access Article

        6 - Representing a Content-based link Prediction Algorithm in Scientific Social Networks
        hosna solaimannezhad omid fatemi
        Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. A Full Text
        Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, link prediction means predicting edges that will be created between nodes in the future. The output of link prediction algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on link prediction that use content published by nodes for predicting collaboration between them. In this study, a new link prediction algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this algorithm predicts their communication in future. The results of tests on SID dataset as coauthor dataset show that developed algorithm outperforms all the structure-based link prediction algorithms. Finally, the reasons of algorithm’s efficiency are analyzed and presented Manuscript Document
      • Open Access Article

        7 - Publication Venue Recommendation Based on Paper’s Title and Co-authors Network
        Ramin Safa Seyed Abolghassem Mirroshandel Soroush Javadi Mohammad Azizi
        Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying Full Text
        Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domain, such as paper recommendation, expert recommendation, citation recommendation and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. The results of evaluation using real-world data show acceptable accuracy in venue recommendations. Manuscript Document
      • Open Access Article

        8 - A Multi-objective Multi-agent Optimization Algorithm for the Community Detection Problem
        Amirhossein Hosseinian Vahid Baradaran
        This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, whil Full Text
        This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, while they have a lower density of between-group connections. Due to high practical usage of community detection in scientific fields, many researchers developed different algorithms to meet various scientific requirements. However, single-objective optimization algorithms may fail to detect high quality communities of complex networks. In this paper, a novel multi-objective Multi-agent Optimization Algorithm, named the MAOA is proposed to detect communities of complex networks. The MAOA aims to optimize modularity and community score as objective functions, simultaneously. In the proposed algorithm, each feasible solution is considered as an agent and the MAOA organizes agents in multiple groups. The MAOA uses new search operators based on social, autonomous and self-learning behaviors of agents. Moreover, the MAOA uses the weighted sum method (WSM) in finding the global best agent and leader agent of each group. The Pareto solutions obtained by the MAOA is evaluated in terms of several performance measures. The results of the proposed method are compared with the outputs of three meta-heuristics. Experiments results based on five real-world networks show that the MAOA is more efficient in finding better communities than other methods. Manuscript Document
      • Open Access Article

        9 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
        Alireza Mansouri Fattaneh Taghiyareh
        People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marke Full Text
        People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links. Manuscript Document