A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network.

Area of Science:

• Network science
• Computer science
• Systems engineering

Background:

• Network robustness is vital for system resilience against failures and attacks.
• Current methods for evaluating network robustness provide a single metric, which is insufficient for comprehensive analysis.
• Assessing network robustness is technically challenging, necessitating advanced evaluation techniques.

Purpose of the Study:

• To propose a novel multitask analysis system (GIN-MAS) for evaluating network robustness.
• To develop a system capable of simultaneously assessing multiple network robustness metrics.
• To improve the accuracy and efficiency of network robustness evaluation.

Main Methods:

• Formulated a destruction-based robustness metric using the destruction threshold.
• Employed a multitask learning approach to learn connectivity robustness, controllability robustness, destruction threshold, and maximum connected components.
• Constructed a five-layer graph isomorphism network (GIN) for simultaneous evaluation of four robustness metrics.

Main Results:

• GIN-MAS demonstrated superior performance over nine other methods, including CNN-based evaluators, across diverse network types.
• The multitask learning scheme facilitated parameter and knowledge sharing, preventing overfitting and enhancing performance.
• GIN-MAS achieved significantly faster multitask evaluations compared to single-task evaluators.

Conclusions:

• GIN-MAS offers a more comprehensive and efficient approach to network robustness evaluation.
• Multitask learning enhances robustness assessment by enabling cross-task knowledge sharing.
• Deep neural networks, particularly GIN-MAS, show significant potential for complex network analysis and robustness evaluation.