A multi-factorial evolutionary algorithm concerning diversity information for solving the multitasking Robust Influence Maximization Problem on networks

ABSTRACT

In recent years, one of the prominent research areas in the complex network field has been the Influence Maximization Problem. This problem focuses on selecting seed sets to achieve optimal information propagation across networks. Practical networks often encounter challenges like node or link failures due to internal issues or external disturbances. Addressing this, researchers emphasize seed robustness against potential interferences, framing it as the robust influence maximisation problem. However, current approaches to this problem are incomplete, leaving several challenges unaddressed. On one hand, existing methods primarily handle seed selection under isolated disruptions, neglecting the simultaneous threats posed by both node-based and link-based attacks. On the other hand, prevailing algorithms fail to capture information synergy from multiple scenarios during the solution process. To bridge these gaps, this study integrates the multi-tasking optimisation theory into robust influence maximisation, introducing an evolutionary algorithm called DMFEA. DMFEA concurrently addresses multiple optimization scenarios, leveraging synergy between tasks while emphasizing information diversity. Experimental results demonstrate DMFEA's competitive edge over existing methods. This research significantly advances collaborative optimization for robust influence maximisation under multi-scenario disruptions, offering a reliable solution for robust information diffusion in complex environments.

KEYWORDS:

• Complex networks
• influence maximisation problem
• robustness
• evolutionary algorithm
• multitasking optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 62203477]; Guangdong Basic and Applied Basic Research Foundation: [Grant Number 2021A1515110543]; Fundamental Research Funds for the Central Universities, Sun Yat-sen University: [Grant Number 23qnpy72]; the Shenzhen Science and Technology Program: [Grant Number JCYJ20220818102012024].