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دسترسی آزاد مقاله
1 - Utilizes the Community Detection for Increase Trust using Multiplex Networks
Rahimeh Habibi Ali Haroun AbadiToday, 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 combini چکیده کاملToday, 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%. پرونده مقاله -
دسترسی آزاد مقاله
2 - کشف جوامع در شبکه‌های اجتماعی با استفاده از کاوش الگوی تکرارشونده
سید احمد موسوی مهرداد جلالی نگین میثاقیانامروزه وب سایت‌های شبکه های اجتماعی به یک منبع غنی از داده های ناهمگون مبدل شده است؛ از این رو تجزیه و تحلیل این داده‌ها می‌تواند منجر به کشف اطلاعات و روابط ناشناخته در این شبکه‌ها شود. کشف جامعه متشکل از گره های مشابه یک چالش مهم در زمینه تجزیه و تحلیل داده های شبکه ه چکیده کاملامروزه وب سایت‌های شبکه های اجتماعی به یک منبع غنی از داده های ناهمگون مبدل شده است؛ از این رو تجزیه و تحلیل این داده‌ها می‌تواند منجر به کشف اطلاعات و روابط ناشناخته در این شبکه‌ها شود. کشف جامعه متشکل از گره های مشابه یک چالش مهم در زمینه تجزیه و تحلیل داده های شبکه های اجتماعی است، و به طور گسترده ای در زمینه ساختار گرافی در این شبکه‌ها مورد مطالعه قرار گرفته است. شبکه های اجتماعی اینترنتی علاوه بر ساختار گرافی، حاوی اطلاعات مفیدی از کاربران درون شبکه می‌باشند؛ که استفاده از این اطلاعات می‌تواند منجر به بهبود کیفت کشف جوامع گردد. در این مقاله یک روش به منظور کشف جامعه ارائه شده است که علاوه بر اطلاعات ارتباطی بین گره ها از اطلاعات محتوایی به منظور ارتقا کیفیت کشف جوامع استفاده می‌گردد. این روش یک رویکرد جدید مبتنی بر الگوی تکرار شونده و بر اساس عملیات کاربران در شبکه است و به طور خاص، بر روی شبکه های اجتماعی اینترنتی که در آن کاربران عملیات مورد علاقه خود را انتخاب می کنند، اجرا می‌شود. ابتدا، بر اساس علایق و یا فعالیت های کاربران در شبکه، تعدادی جوامع کوچک متشکل از کاربران مشابه را کشف می کنیم و سپس با استفاده از ارتباطات اجتماعی هر جامعه را گسترش می دهیم. نتایج ارزیابی F-measure بر روی دو مجموعه داده دنیای واقعی (بلاگ کاتالوگ و فلیکر) نشان می‌دهد که روش پیشنهادی منجر به بهبود کیفیت کشف جوامع می شود. پرونده مقاله -
دسترسی آزاد مقاله
3 - ارائه روشی مبتنی بر رتبه‌بندی و الگوریتم انتشار گرما برای استخراج جامعه در شبکه‌های اجتماعی
ملیحه ابراهیمی نژاد سمانه حسن زاده مهرداد جلالیشبکههای اجتماعی در دهه گذشته توسعه سریعی داشته‌اند. استخراج جامعه مسئله مهمی در تحلیل شبکه‌های اجتماعی است. کشف جامعه در شبکه اجتماعی به‌معنای شناسایی مجموعه‌‌ای از گره‌هاست به‌گونه‌ای که اعضای آن بیشترین ارتباط را بایکدیگر و ارتباط کمی با گره‌های خارج از مجموعه دارند چکیده کاملشبکههای اجتماعی در دهه گذشته توسعه سریعی داشته‌اند. استخراج جامعه مسئله مهمی در تحلیل شبکه‌های اجتماعی است. کشف جامعه در شبکه اجتماعی به‌معنای شناسایی مجموعه‌‌ای از گره‌هاست به‌گونه‌ای که اعضای آن بیشترین ارتباط را بایکدیگر و ارتباط کمی با گره‌های خارج از مجموعه دارند. الگوریتم کلاسیک خوشه‌بندی Kمیانه روش کارایی در این حوزه است ولی این الگوریتم حساس به نقاط اولیه ورودی است. برای حل این مشکل دراین پژوهش ابتدا K نقطه ثقل با استفاده از PageRank شناسایی و سپس ساختار جامعه با استفاده از شباهت انتشارگرما وK میانه استخراج می‌شود. با استفاده از شباهت انتشارگرما می‌توان اطلاعات سراسری شبکه را برای هر زوج از رأس‌ها بدست و برای استفاده در شبکه‌های بزرگ و خلوت مناسب است. پرونده مقاله -
دسترسی آزاد مقاله
4 - An Extended Louvain Method for Community Detection in Attributed Social Networks
Yasser Sadri Saeid Taghavi Afshord shahriar lotfi Vahid MajidnezhadCommunity detection is a significant way to analyze complex networks. Classical methods usually deal only with the network's structure and ignore content features. During the last decade, most solutions for community detection only consider network topology. Social netw چکیده کاملCommunity detection is a significant way to analyze complex networks. Classical methods usually deal only with the network's structure and ignore content features. During the last decade, most solutions for community detection only consider network topology. Social networks, as complex systems, contain actors with certain social connections. Moreover, most real-world social networks provide additional data about actors, such as age, gender, preferences, etc. However, content-based methods lead to the loss of valuable topology information. This paper describes and clarifies the problems and proposes a fast and deterministic method for discovering communities in social networks to combine structure and semantics. The proposed method has been evaluated through simulation experiments, showing efficient performance in network topology and semantic criteria and achieving proportional performance for community detection. پرونده مقاله -
دسترسی آزاد مقاله
5 - A New Multi-Agent Bat Approach for Detecting Community Structure in Social Networks
Saeed Alidoost Behrooz MasoumiThe complex networks are widely used to demonstrate effective systems in the fields of biology and sociology. One of the most significant kinds of complex networks is social networks. With the growing use of such networks in our daily habits, the discovery of the hidden چکیده کاملThe complex networks are widely used to demonstrate effective systems in the fields of biology and sociology. One of the most significant kinds of complex networks is social networks. With the growing use of such networks in our daily habits, the discovery of the hidden social structures in these networks is extremely valuable because of the perception and exploitation of their secret knowledge. The community structure is a great topological property of social networks, and the process to detect this structure is a challenging problem. In this paper, a new approach is proposed to detect non-overlapping community structure. The approach is based on multi-agents and the bat algorithm. The objective is to optimize the amount of modularity, which is one of the primary criteria for determining the quality of the detected communities. The results of the experiments show the proposed approach performs better than existing methods in terms of modularity. پرونده مقاله -
دسترسی آزاد مقاله
6 - Overlapping Community Detection in Social Networks Based on Stochastic Simulation
Hadi Zare Mahdi HajiabadiCommunity detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bi چکیده کاملCommunity detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on different metrics and domain of applications. Most of these methods are based on the existing of the non-overlapping or sparse overlapping communities. Moreover, the experimental analysis showed that, overlapping areas of communities become denser than non-overlapping area of communities. In this paper, significant methods of overlapping community detection are compared according to well-known evaluation criteria. The experimental analyses on artificial network generation have shown that earlier methods of community detection will not discover overlapping communities properly and we offered suggestions for resolving them. پرونده مقاله -
دسترسی آزاد مقاله
7 - An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks
Hasan Rabani Farhad Soleimanian GharehchopoghThe structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of commun چکیده کاملThe structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of community detection is to separate groups or communities that are linked more closely. In fact, community detection is the clustering of the network, and the community separates a graph. In recent years, public methods suffer from inefficiency because of the high complexity of time and the need for full access to graph information. In contrast, smart methods such as meta-heuristic algorithms, the use of low parameters and much less complex time complexity have been among the most popular methods in recent years. These methods have good features, but they still face problems such as dependence on finding the best point in search space, global updates, and poor quality due to the formation of large communities and others. In this paper, in order to improve the mentioned problems, a method is proposed based on combining the Firefly Algorithm (FA) and Learning Automata (LA). In the proposed model, LA is used to increase the efficiency of the FA. Choosing the best neighbours for the FA agents is done using the LA. The results from the four datasets of Karate, Dolphins, Polbooks, and Football show that the proposed model has more Normalized Mutual Information (NMI) than other models. پرونده مقاله -
دسترسی آزاد مقاله
8 - An Improved Symbiotic Organisms Search for Community Detection in Social Networks
Yahya Ghanbarzadeh BonabResearch on network Community Detection (CD) has predominantly focused on identifying communities of densely connected nodes in undirected networks. Community structure is an integral part of a social network. Detecting such communities plays a vital role in a wide rang چکیده کاملResearch on network Community Detection (CD) has predominantly focused on identifying communities of densely connected nodes in undirected networks. Community structure is an integral part of a social network. Detecting such communities plays a vital role in a wide range of applications, including but not limited to cluster analysis, recommendation systems, and understanding the behavior of complex systems. Researchers have derived many algorithms from discovering the community structures of networks. Finding communities is a challenging task, and there is no single algorithm that produces the best results for all networks. Therefore, despite many elegant solutions, learning communities remain active research areas.CD is a challenging optimization problem that consists in searching for communities that belong to a network or graph under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Many methods have been proposed to address this problem in many research fields, such as power systems, biology, sociology, and physics. Many of those optimization methods use modularity to identify the optimal network subdivision. This paper proposes a new CD approach based on Symbiotic Organisms Search (SOS) and Lévy Flight (LF). The LF distribution is used to prevent the stagnation of solutions in local minima. Extensive experiments compare the SOS-LF with other state-of-the-art algorithms on real-world social networks. Experimental results show that the SOS-LF is effective and stable. پرونده مقاله -
دسترسی آزاد مقاله
9 - Using The Gray Wolf Optimization Algorithm for Community Detection
Maliheh Ghasemzadeh Mohammad Amin GhasemzadehIn 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 چکیده کاملIn 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. پرونده مقاله
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