Excavating important nodes in complex networks based on the heat conduction model

Area of Science:

• Complex Systems Analysis
• Network Science

Background:

• Identifying critical nodes is crucial for understanding and managing complex systems.
• Existing methods for node importance excavation face limitations in accuracy and scope.

Purpose of the Study:

• To propose a new algorithm for excavating important nodes in complex networks.
• To evaluate the proposed algorithm's effectiveness using established network models and real-world networks.

Main Methods:

• Developed a novel algorithm based on the heat conduction model (HCM) to measure node importance via output capacity.
• Incorporated network density, inter-node distance, and degree density into the capacity calculation.
• Validated the algorithm using Susceptible-Infected-Removed (SIR) and Independent Cascade (IC) models across nine real networks.

Main Results:

• The HCM-based algorithm demonstrated superior performance in identifying important nodes compared to eight other algorithms.
• Experiments confirmed the algorithm's effectiveness in similarity, sorting, and multi-node infection capability assessments.
• The model accurately reflects node importance by considering neighbor influence and network topology.

Conclusions:

• The proposed heat conduction model (HCM) offers a robust and effective approach for excavating important nodes in complex networks.
• This method provides a valuable tool for analyzing complex systems and addressing scientific bottlenecks.
• The findings highlight the significance of output capacity in determining node criticality within network structures.