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| Format: | Preprint |
| Published: |
2020
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| Online Access: | https://arxiv.org/abs/2012.13617 |
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| _version_ | 1866916343103619072 |
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| author | Amshi, Auwal Tijjani |
| author_facet | Amshi, Auwal Tijjani |
| contents | There is great significance in evaluating a node's Influence ranking in complex networks. Over the years, many researchers have presented different measures for quantifying node interconnectedness within networks. Therefore, this paper introduces a centrality measure called Tr-centrality which focuses on using the node triangle structure and the node neighborhood information to define the strength of a node, which is defined as the summation of Gruebler's Equation of the node's one-hop triangle neighborhood to the number of all the edges in the subgraph. Furthermore, we socially consider it as the local trust of a node. To verify the validity of Tr-centrality [1], we apply it to four real-world networks with different densities and shapes, and Tr-centrality has proven to yield better results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_13617 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | A New Perspective to Node Influence Evaluation in Complex Network Using Subgraph Tr-Centrality Amshi, Auwal Tijjani Social and Information Networks There is great significance in evaluating a node's Influence ranking in complex networks. Over the years, many researchers have presented different measures for quantifying node interconnectedness within networks. Therefore, this paper introduces a centrality measure called Tr-centrality which focuses on using the node triangle structure and the node neighborhood information to define the strength of a node, which is defined as the summation of Gruebler's Equation of the node's one-hop triangle neighborhood to the number of all the edges in the subgraph. Furthermore, we socially consider it as the local trust of a node. To verify the validity of Tr-centrality [1], we apply it to four real-world networks with different densities and shapes, and Tr-centrality has proven to yield better results. |
| title | A New Perspective to Node Influence Evaluation in Complex Network Using Subgraph Tr-Centrality |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2012.13617 |