Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength
1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.
Entropy (Basel, Switzerland)
|May 27, 2023
Summary
This study introduces a novel graph neural network (GNN) model to accurately identify influential nodes in complex networks by considering relationship strengths. The enhanced GNN model improves information aggregation for better node influence identification.
Area of Science:
- Network Science
- Computer Science
- Data Science
Background:
- Identifying influential nodes is crucial in complex network analysis.
- Existing graph neural networks (GNNs) often overlook the varying strengths of relationships between nodes.
- This limitation hinders accurate node influence assessment in diverse networks.
Purpose of the Study:
- To develop an improved GNN model for more effective identification of influential nodes.
- To address the limitations of current GNNs in handling relationship strengths and network diversity.
Main Methods:
- Constructed node input features using information entropy, node degree, and average neighbor degree.
- Developed a GNN model that incorporates neighborhood overlap to determine relationship strength for message passing.
- Validated the model on 12 real-world networks using the SIR model for influence assessment.
Main Results:
- The proposed GNN model effectively aggregates node and neighborhood information by considering relationship strengths.
- Experimental results demonstrate superior performance compared to benchmark methods in identifying influential nodes.
- The model shows adaptability across diverse complex network structures.
Conclusions:
- The novel GNN approach significantly enhances the accuracy of influential node identification in complex networks.
- Considering relationship strengths and employing diverse node features are key to improving GNN performance.
- This method offers a more robust tool for analyzing influence dynamics in networked systems.
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