Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network
In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8715619 |
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| _version_ | 1832567805987258368 |
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| author | Xiaolong Deng Hao Ding Yong Chen Cai Chen Tiejun Lv |
| author_facet | Xiaolong Deng Hao Ding Yong Chen Cai Chen Tiejun Lv |
| author_sort | Xiaolong Deng |
| collection | DOAJ |
| description | In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and IMpv (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for IMpv. Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and IMpv based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of IMpv. To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network. |
| format | Article |
| id | doaj-art-83eb47ade4464964ae77251be2614a88 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-83eb47ade4464964ae77251be2614a882025-02-03T01:00:28ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/87156198715619Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed NetworkXiaolong Deng0Hao Ding1Yong Chen2Cai Chen3Tiejun Lv4Key Lab of Trustworthy Distributed Computing and Service of Education Ministry, Beijing University of Post and Telecommunication, Beijing 100876, ChinaKey Lab of Trustworthy Distributed Computing and Service of Education Ministry, Beijing University of Post and Telecommunication, Beijing 100876, ChinaNorth Automatic Control Technology Institute, Taiyuan, ChinaChina Academy of Information and Communications Technology (CAICT), Beijing 100037, ChinaKey Lab of Trustworthy Distributed Computing and Service of Education Ministry, Beijing University of Post and Telecommunication, Beijing 100876, ChinaIn recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and IMpv (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for IMpv. Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and IMpv based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of IMpv. To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.http://dx.doi.org/10.1155/2020/8715619 |
| spellingShingle | Xiaolong Deng Hao Ding Yong Chen Cai Chen Tiejun Lv Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network Complexity |
| title | Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network |
| title_full | Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network |
| title_fullStr | Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network |
| title_full_unstemmed | Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network |
| title_short | Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network |
| title_sort | novel node centrality based efficient empirical robustness assessment for directed network |
| url | http://dx.doi.org/10.1155/2020/8715619 |
| work_keys_str_mv | AT xiaolongdeng novelnodecentralitybasedefficientempiricalrobustnessassessmentfordirectednetwork AT haoding novelnodecentralitybasedefficientempiricalrobustnessassessmentfordirectednetwork AT yongchen novelnodecentralitybasedefficientempiricalrobustnessassessmentfordirectednetwork AT caichen novelnodecentralitybasedefficientempiricalrobustnessassessmentfordirectednetwork AT tiejunlv novelnodecentralitybasedefficientempiricalrobustnessassessmentfordirectednetwork |