A Novel Node Importance Evaluation Method Based on Agglomeration Contraction Principle for Wireless Sensor Networks
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TL;DR: Using some superenergy nodes to provide targeted protection for the vital gateway nodes in the network, the life of the network can be prolonged and the robustness of the system improved effectively.
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Abstract: The node importance evaluation based on removal of nodes and their incident links cannot accurately reflect
the importance of the nodes, because such approach may change the topology of the network, and even split the
network into several disconnected parts. To solve this problem, we propose a novel node importance evaluation
method based on agglomeration contraction principle. This method does not require the node being evaluated to be
removed from the network; thus, it may not cause network fracture. With this method, aided by spectral analysis,
the vital gateway nodes can be identified by using the nontrivial eigenvectors of the Laplace matrix of the network
diagram. Then, a formula to estimate the importance of particular individuals within the network is proposed;
moreover, the betweenness centrality and the positions of nodes are taken into consideration. At last, using some
superenergy nodes to provide targeted protection for the vital gateway nodes in the network, the life of the
network can be prolonged and the robustness of the system improved effectively. Above all, with this method, the
number of nodes to be evaluated in a network can be reduced, and the computation decreased accordingly. Final
experiments verify the efficiency of the proposed method and the result is consistent with our intuitive judgments.
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References
Virtual network embedding through topology-aware node ranking
Xiang Cheng,Sen Su,Zhongbao Zhang,Hanchi Wang,Fangchun Yang,Yan Luo,Jie Wang +6 more
- 15 Apr 2011
TL;DR: The Markov Random Walk model is applied to rank a network node based on its resource and topological attributes and shows that the topology-aware node rank is a better resource measure and the proposed RW-based algorithms increase the long-term average revenue and acceptance ratio.
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Evaluation of Node Importance in Complex Networks
TL;DR: To improve the efficiency and validity of node importance evaluating, a new evaluation method based on node closeness and node key degree in its neighborhood was proposed, which found that the bigger the closeness of a node is, the closer to center of a complex network the node is and the more important it is.
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Improved evaluation method for node importance based on node contraction in weighted complex networks
TL;DR: A new definition of weighted node importance is proposed, and an improved node contraction method in weighted networks is given based on the evaluation criterion that can help exactly to find some critical nodes in complex networks.
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