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Volume 42 Issue 3
Mar.  2020
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Kai WANG, Xing LI, Julong LAN, Hongquan WEI, Shuxin LIU. A New Link Prediction Method for Complex Networks Based onTopological Effectiveness of Resource Transmission Paths[J]. Journal of Electronics & Information Technology, 2020, 42(3): 653-660. doi: 10.11999/JEIT190333
Citation: Kai WANG, Xing LI, Julong LAN, Hongquan WEI, Shuxin LIU. A New Link Prediction Method for Complex Networks Based onTopological Effectiveness of Resource Transmission Paths[J]. Journal of Electronics & Information Technology, 2020, 42(3): 653-660. doi: 10.11999/JEIT190333

A New Link Prediction Method for Complex Networks Based onTopological Effectiveness of Resource Transmission Paths

doi: 10.11999/JEIT190333
Funds:  The National Natural Science Foundation of China (61803384), The National Natural Science Foundation Innovation Research Group Project of China (61521003)
  • Received Date: 2019-05-13
  • Rev Recd Date: 2019-09-10
  • Available Online: 2019-09-19
  • Publish Date: 2020-03-19
  • Link prediction considers to discover the unknown or missing links of complex networks by using the existing topology or other information. Resource Allocation index can achieve a good performance with low complexity. However, it ignores the path effectiveness of resource transmission process. The resource transmission process is an important internal driving force for the evolution of the network. By analyzing the effectiveness of the topology around the resource transmission path between nodes, a link prediction method based on topological effectiveness of resource transmission paths is proposed. Firstly, the influence of potential resource transmission paths between nodes on resource transmission is analyzed, and a quantitative method for resource transmission path effectiveness is proposed. Then, based on the effectiveness of the resource transmission path, after studying the two-way resource transmission amount between two nodes, the transmission path effectiveness index is proposed. The experimental results of 12 real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under the AUC and Precision metrics.

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