Recently, the community analysis has seen enormous research advancements in the field of social networks. A large amount of the current studies put forward different models and algorithms about most influential people...Recently, the community analysis has seen enormous research advancements in the field of social networks. A large amount of the current studies put forward different models and algorithms about most influential people. However, there is little work to shed light on how to rank communities while considering their levels that are determined by the quality of their published contents. In this paper, we propose solution for measuring the influence of communities and ranking them by considering joint weight composed of internal and external influence of communities. To address this issue, we design a novel algorithm called Com Rank: a modification of Page Rank, which considers the joint weight in order to identify impact of each community and ranking them. We use real-world data trace in citation network and perform extensive experiments to evaluate our proposed algorithm. The comparative results depict significant improvements by our algorithm in community ranking due to the inclusion of proposed weighting feature.展开更多
Over the past few years,many efforts have been dedicated to studying cyberbullying in social edge computing devices,and most of them focus on three roles:victims,perpetrators,and bystanders.If we want to obtain a deep...Over the past few years,many efforts have been dedicated to studying cyberbullying in social edge computing devices,and most of them focus on three roles:victims,perpetrators,and bystanders.If we want to obtain a deep insight into the formation,evolution,and intervention of cyberbullying in devices at the edge of the Internet,it is necessary to explore more fine-grained roles.This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means(DEK)method to identify diverse roles.Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings.The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level.After clustering,we obtain nine roles and analyze the characteristics of each role and their evolution trends under different cyberbullying scenarios.Our work in this paper can be placed in devices at the edge of the Internet,leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices.展开更多
基金supported in part by the following funding agencies of China:National Natural Science Foundation under Grant 61170274, 61602050 and U1534201
文摘Recently, the community analysis has seen enormous research advancements in the field of social networks. A large amount of the current studies put forward different models and algorithms about most influential people. However, there is little work to shed light on how to rank communities while considering their levels that are determined by the quality of their published contents. In this paper, we propose solution for measuring the influence of communities and ranking them by considering joint weight composed of internal and external influence of communities. To address this issue, we design a novel algorithm called Com Rank: a modification of Page Rank, which considers the joint weight in order to identify impact of each community and ranking them. We use real-world data trace in citation network and perform extensive experiments to evaluate our proposed algorithm. The comparative results depict significant improvements by our algorithm in community ranking due to the inclusion of proposed weighting feature.
基金supported by the National Key Research and Development Program of China(No.2021YFC3300202)faculty of Shanghai Key Laboratory of Data Science,Fudan Institute on Aging,MOE Laboratory for National Development and Intelligent GovernanceShanghai Institute of Intelligent Electronics&Systems,Fudan University.
文摘Over the past few years,many efforts have been dedicated to studying cyberbullying in social edge computing devices,and most of them focus on three roles:victims,perpetrators,and bystanders.If we want to obtain a deep insight into the formation,evolution,and intervention of cyberbullying in devices at the edge of the Internet,it is necessary to explore more fine-grained roles.This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means(DEK)method to identify diverse roles.Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings.The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level.After clustering,we obtain nine roles and analyze the characteristics of each role and their evolution trends under different cyberbullying scenarios.Our work in this paper can be placed in devices at the edge of the Internet,leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices.
