.png){kind=icon}
.png){kind=icon}
Return
A multi-dimensional eigenvector and cycle ratio model for identifying important nodes
.png){kind=icon}
PRE
AI
DOI：10.1080/17445760.2025.2594043.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
How to identify influential nodes is an open question. Cycle ratio, which is used to reflect the dynamic role of cycles, is to measure the importance of nodes. However, one dimension, which relying solely on cycle ratio, for measurement produces unreliable outcomes. For complex networks, even if nodes have the same cycle ratio, there are significant differences in their actual impact on the network due to their unique connection methods and locations. By considering the cycle ratio as the mass of the node in the gravity model, the maximum eigenvector of the node is used as a weight to reflect the global influence. Therefore, we propose a multi-dimensional eigenvector and cycle fusion gravity model (ECGM). The ECGM model achieves accurate identification of the importance of network nodes by combining the number of node adjacency, connection characteristics, and integrating local and global information. SIR and Kendall's Tau simulation experiments are conducted on six real networks, and the results showed that the elasticity of the ECGM algorithm was better than that of the classical algorithm and the gravity model, which was reflected in various experimental indicators.
Keywords:
Complex network
node importance
cycle ratio
gravity model
Journal
I
IF:
0.7
Papers:
50
Citations:
266