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• W3211739794 abstract "Online social networks have gained tremendous popularity and have dramatically changed the way we communicate in recent years. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. Therefore, the robustness analysis of network topologies has been a hot research topic in recent years. To characterize the structure feature of complex social network quantificationally, we propose a new measure for network robustness, namely, network cluster entropy, which takes the impact of cluster density on the network structure into consideration. Besides, the relationship between the network cluster entropy and network connectivity reliability is established. To show the effectiveness of the proposed method, we compute the network cluster entropy of the Zachary’s Karate Club network with two existing indices under disparate divisions, and we also compare the results by measuring the ability of each index to characterize network heterogeneity. Both of experimental results and empirical analysis show that our proposed method has the more excellent performance compared with two existing methods. Therefore, it is indicated that the cluster entropy and network connectivity reliability will be an important tool to study the online social network." @default.
• W3211739794 created "2021-11-22" @default.
• W3211739794 creator A5006133101 @default.
• W3211739794 creator A5088259893 @default.
• W3211739794 date "2021-01-01" @default.
• W3211739794 modified "2023-10-16" @default.
• W3211739794 title "A New Measure of Network Robustness: Network Cluster Entropy" @default.
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• W3211739794 doi "https://doi.org/10.1007/978-981-16-7913-1_13" @default.
• W3211739794 hasPublicationYear "2021" @default.
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