جستجوی مقالات مرتبط با کلیدواژه "تحلیل شبکه اجتماعی" در نشریات گروه "فنی و مهندسی"

The fourth industrial revolution, with the convergence of new technologies, has led to the emergence of smart cities. While the smartening of cities by governments has been happening at a high speed, the concept and implementation of the smart city are still being updated and changed. In this regard, the current research aims to analyze the thematic trends of publications on smart city in the Web of Science (WoS) database from the emergence of the fourth industrial revolution to 2021, and track the thematic trends of these studies. This scientometric research was carried out descriptively with the content analysis method, using the techniques of co-occurrence analysis and social network analysis. The trends of publications and citation influence of studies in the field of smart cities in the last decade has had an upward growth of 38.78% and 69.49%, respectively. In the three time periods, "IoT, City, Internet, and Wireless Sensor Network" have the most frequency, "IOT, City, Internet, and Wireless Sensor Network" have the most connections, and "ipv6, Sustainable City, and Urban Development" in 2012–2015, "Taxonomy, cloud objects and distributed computing" in 2016–2018 and "Literature review, urban informatics, and sustainable urban development" in 2019-2021 have received the most citations. In 2012–2015, clusters of sensor networks, smartphones, genetic algorithms, advanced sensors, ubiquitous city, and m2m; in 2016-2018 clusters of deep learning, participation, evolutionary algorithms, ubiquitous computing, and smart cities; and in 2019-2021 blockchain clusters, participation citizenship, IoT, topics, technologies, and tools.multi-agent systems, and Brazil were identified.

Interactions and conflicts, or in general, the relationships between different stakeholders in a large-scale water resources scheme, are very important factors affecting the status of water systems. In this study, the conflict was studied, mapped, and rooted in a water system using the social network analysis approach, utility analysis, and conflict tree. After finding the major problems in a water system, influential stakeholders were first identified. Then, using the social network analysis method the relationships between these stakeholders were studied to estimate their conflicts. Next, by assessing the desirability of stakeholders, the ground of conflict emerging between them was investigated using desirability mapping. Using the output of the desirability analysis, social network analysis, and important problems recognition, the roots, core, and results of the conflict were presented using the conflict tree approach. The catchment area in the west of Tehran, namely the Kan River basin, has been studied using the mentioned method. The little interest of the municipal institution in utilizing groundwater resources, the problem of subsidence and the quality of water resources, coupled with the interest in expanding green spaces and urban development are among the challenge between the regional municipality and the regional water authority. The minor consideration for the management of illegal water extraction, return water, aquatic ecosystems, and water quality has been the cause of conflict between the agricultural institution and the regional water authority. It is also critical that the interest of the industrial sector is focused on development of industries and other interests are of less importance in this sector. The lack of interest in institutions such as the urban development stakeholder to consider the water cycle's importance in urban development is another challenging issue.

An analysis of social networks is necessary to detect anomalous users, due to the popularity of these networks. This paper aims to detect anomalous users in location based social networks. For this purpose, an ego graph is computed for each user, and the five variables vertex degree, edge size, edge weight, betweenness centrality and eigenvector centrality are calculated with respect to the weights of the edges in this graph. Then six relationships between two of these variables are made up, and for each of these relationships, the line equation is obtained in the coordinate system of the two variables. This equation is used to predict the value of the variables. Based on this predicted value, the user's score is determined, and anomalous users are detected. The proposed method investigates anomalies in the friendship graph, location of residence and interests of users. The results indicate that the proposed method has been able to detect anomalous users by examining the scores of star and clique structures in the graph.

Social network analysis’ link prediction has a diverse range of applications in different areas of science. Bipartite networks are a kind of complex network, which can be used to describe various real-world phenomena. In this article, a link prediction method for bipartite network is presented. Uni-partite link prediction methods are not effective and efficient enough to be applied to bipartite networks. Thus, to solve this problem, distinct methods specifically designed for bipartite networks are required. The proposed method is neighbor based and consisted of measures of such. Classic uni-partite link prediction measures are redefined to be compatible with bipartite network. Subsequently, these modified measures are used as the basis of the presented method, which in addition to simplicity, has high performance rates and is superior to other neighbor-based methods by 15% in average.

An analysis of social networks is necessary to detect anomalous users, due to the popularity of these networks. This paper aims to detect anomalous users in location based social networks. For this purpose, an ego graph is computed for each user, and the five variables vertex degree, edge size, edge weight, betweenness centrality and eigenvector centrality are calculated with respect to the weights of the edges in this graph. Then six relationships between two of these variables are made up, and for each of these relationships, the line equation is obtained in the coordinate system of the two variables. This equation is used to predict the value of the variables. Based on this predicted value, the user's score is determined, and anomalous users are detected. The proposed method investigates anomalies in the friendship graph, location of residence and interests of users. The results indicate that the proposed method has been able to detect anomalous users by examining the scores of star and clique structures in the graph.

An analysis of social networks is necessary to detect anomalous users, due to the popularity of these networks. This paper aims to detect anomalous users in location based social networks. For this purpose, an ego graph is computed for each user, and the five variables vertex degree, edge size, edge weight, betweenness centrality and eigenvector centrality are calculated with respect to the weights of the edges in this graph. Then six relationships between two of these variables are made up, and for each of these relationships, the line equation is obtained in the coordinate system of the two variables. This equation is used to predict the value of the variables. Based on this predicted value, the user's score is determined, and anomalous users are detected. The proposed method investigates anomalies in the friendship graph, location of residence and interests of users. The results indicate that the proposed method has been able to detect anomalous users by examining the scores of star and clique structures in the graph.