MFEA-RCIM: A Multifactorial Evolutionary Algorithm for Determining Robust and Influential Seeds From Competitive Networks Under Structural Failures

Networks objectively portray functional distributions in practical systems, streamlining optimization and information extraction from typological structures. Recent studies have intensified scrutiny of the robust competitive influence maximization (RCIM) problem, focusing on identifying the most impactful seed set for effective and robust propagation. Literature offers performance metrics and algorithms that integrate diverse groups, suggesting potential synergy among them and the value of diverse candidates for balanced group performance. However, a thorough study toward the RCIM problem is still pendent, and a well-developed paradigm for attaining the equilibrium across groups is in demand. This article addresses these challenges by introducing multitask optimization in competitive network seed determination. A multitask framework is constructed, encompassing distinct diffusion scenarios for multiple groups and the network as a whole. To tackle this problem, we develop a Multi-Factorial Evolutionary Algorithm for RCIM (MFEA-RCIM). MFEA-RCIM leverages dedicated operators to exploit task parallelism and fosters competition among diffusion groups through a transfer operation. Experimental results on synthetic and practical networks demonstrate that MFEA-RCIM outperforms existing methods, with efficiency gains attributed to the multitasking optimization strategy.