Node Importance Identification for Temporal Networks Based on Optimized Supra-Adjacency Matrix

Abstract

The research on node importance identification for temporal networks has attracted much attention. In this work, combined with the multi-layer coupled network analysis method, an optimized supra-adjacency matrix (OSAM) modeling method was proposed. In the process of constructing an optimized super adjacency matrix, the intra-layer relationship matrixes were improved by introducing the edge weight. The inter-layer relationship matrixes were formed by improved similarly and the inter-layer relationship is directional by using the characteristics of directed graphs. The model established by the OSAM method accurately expresses the structure of the temporal network and considers the influence of intra- and inter-layer relationships on the importance of nodes. In addition, an index was calculated by the average of the sum of the eigenvector centrality indices for a node in each layer and the node importance sorted list was obtained from this index to express the global importance of nodes in temporal networks. The experimental results on three real temporal network datasets Enron, Emaildept3, and Workspace showed that compared with the SAM and the SSAM methods, the OSAM method has a faster message propagation rate and larger message coverage and better SIR and NDCG@10 indicators.

Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy

“…This allows us to analyze the influence of nodes within each period based on fine-grained interaction behaviors, which can be further utilized for link prediction. The snapshot interaction frequency matrix C n is calculated using Equation (6).…”

“…By abstracting the sensors used in autonomous driving as nodes, the complex network can be represented as a road topology within a specific area [5]. Thus, a series of complex network techniques can be further used to analyze the target system features, such as node importance evaluation [6], fractal research of complex networks [7], and community detection [8]. As one of the most representative problems in complex networks, link prediction aims to estimate the unknown or missing links possibility between nodes by using the current node connection information of the target network.…”