Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 5; p. 754
• Main Authors: Xi, Ying, Cui, Xiaohui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.05.2023; MDPI
• Subjects: Analysis; Clustering; complex networks; Deep learning; Entropy (Information theory); Graph neural networks; Infections; influential node; information entropy; Machine learning; Message passing; Methods; Neural networks; Nodes; Propagation; relationship strength; SIR; Social networks; information entropy; influential node; complex networks; SIR; relationship strength
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25050754

Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.