HA: An Influential Node Identification Algorithm Based on Hub-Triggered Neighborhood Decomposition and Asymmetric...

Area of Science:

• Power Systems Engineering
• Network Science
• Cybersecurity

Background:

• Power networks face increasing security threats from malicious attacks.
• Identifying influential nodes is crucial for power grid security and stability.
• Complex networks, like power grids, exhibit high clustering coefficients and intricate node interconnections.

Purpose of the Study:

• To propose a novel algorithm for evaluating node influence in power networks.
• To enhance the identification of critical nodes vulnerable to or capable of propagating threats.
• To improve the security and resilience of power grids against cyber-attacks.

Main Methods:

• Introduced network directionalization strategy and hub-triggered neighborhood decomposition.
• Developed concepts of infected and infecting potential.
• Constructed an asymmetric, order-by-order recurrence model for influence calculation.
• Integrated multi-order neighbor potentials for hub node influence evaluation.

Main Results:

• The proposed algorithm demonstrates superior performance compared to traditional and state-of-the-art methods.
• Evaluations on six power networks showed improvements in Susceptible-Infected-Recovered (SIR) correlation coefficients, imprecision functions, and algorithmic resolution.
• The method effectively distinguishes functional node differences in threat propagation processes.

Conclusions:

• The novel node influence evaluation algorithm offers enhanced accuracy and effectiveness for power network security.
• The approach provides a robust framework for identifying critical nodes in complex networks.
• This research contributes to strengthening the cybersecurity of power infrastructure.