An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features

• Published in: Entropy (Basel, Switzerland) Vol. 26; no. 8; p. 676
• Main Authors: Jin, Sheng, Xiao, Yuzhi, Han, Jiaxin, Huang, Tao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10-08-2024; MDPI
• Subjects: Algorithms; Analysis; Artificial neural networks; complex networks; Construction; Copying; Dynamic characteristics; Graph neural networks; Heuristic; heuristics; higher-order zero model; Infections; Influence; Network topologies; Neural networks; node influence; Nodes; Noise control; Optimization; Perception; Propagation; Redundancy; SIR model; Social networks; Structural stability; Systems stability; higher-order zero model; complex networks; heuristics; node influence; SIR model
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e26080676

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.