Evaluation Algorithm for the Importance of Nodes in Directed-Weighted Networks Based on Transfer Capability Matrix

Due to the heterogeneity of the complex network structure, there are some special nodes in the networks. Once these nodes fail, they will cause a large area of network crashes in a short period of time. Therefore, how to identify these special nodes in the network accurately is very important. In this paper, a node importance evaluation algorithm based on Transfer Capability Matrix is proposed. The Contribution Capability Matrix and Load Capability Matrix are defined to reflect the importance of nodes which relative to its neighbors, and then the Transfer Capability Matrix of nodes are calculated according to the above matrices. So the importance evaluation value of each node is obtained by Transfer Capability Matrix. Both considering the contribution ability and the load capacity of nodes, which makes the evaluation process more comprehensive. we also apply our method to ARPA network, Neural network, American aviation network and social network. The experiments show that our method can better mine key nodes in the networks. Finally, the effectiveness of the proposed algorithm is further verified by the cascading failures.

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