TL;DR
A novel method is designed that disrupts the structure of the original network by adding and deleting edges, ensuring an equal number of additions and deletions, with the aim to enhance concealment and address the issue of inadequate deception.
A novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional structural entropy within a potential game framework, and introduces a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.
Community detection is a fundamental task in complex network analysis, enabling the identification of hidden structures and functional groupings within social, biological, and technological systems. Although multi-objective genetic algorithms have been shown to perform well for this task, their practical application is typically constrained by excessive computational expense, especially on large or dense networks. In this paper, we present an efficient hybrid genetic algorithm for multi-objective community detection that makes a good trade-off between solution quality and computational efficiency. Our algorithm incorporates several essential improvements, including a fitness caching technique to prevent duplicate evaluations, a lightweight crossover operator to minimize the overhead of the evolutionary process, and an efficient archive management strategy that prioritizes high-quality solutions along the Pareto front. Experimental evaluations on several benchmark datasets demonstrate that the proposed method achieves comparable or improved modularity (Q) values, maintains stable normalized mutual information (NMI) values, and significantly reduces execution time compared with the previously proposed method, achieving up to 57.7% runtime reduction on large-scale networks. The results confirm that the proposed method provides a viable and scalable approach to community detection in large-scale complex networks
Can cooperation among large language model (LLM) agents be evolutionarily stable against free-rider invasion? We study an indirect reciprocity donation game where LLM agents observe behavioral traces and donate on a continuous scale. Strategies, represented as natural language prompts, evolve through cultural transmission across generations. Across four LLM backends, robustness to free-rider invasion varies by more than an order of magnitude. The strongest predictor of this robustness is opponent endowment sensitivity, the degree to which agents discriminate between cooperative and uncooperative opponents, operationalizing the classical Image Scoring mechanism. By contrast, adherence to the Leading-Eight L1 norm does not predict robustness. Robustness depends on defector exclusion: while both cooperator reward and defector punishment vary across models, only the stringency of defector exclusion predicts resistance to free-rider invasion. These findings reveal that LLM agents are confined to Image Scoring-like discrimination and fail to develop the more robust Leading-Eight norms, highlighting a fundamental vulnerability in culturally evolved LLM cooperation and motivating bottom-up approaches to norm construction.
Influence maximization in social networks has received increasing attention, particularly in applications where fairness among demographic groups is an important concern. However, many existing approaches either overlook group-level disparities or primarily optimize influence spread without explicitly modeling fairness-related trade-offs. In this paper, we propose a group-aware multi-objective evolutionary framework that decomposes seed sets into group-specific sub-solutions. Each demographic group maintains independent subpopulations, which are evolved to improve local influence within the corresponding group. The optimized sub-solutions are then recombined into full solutions, which are globally evaluated using two objectives: total influence spread and inter-group fairness, estimated via pre-generated Reverse Reachable sets. This design reduces the need for repeated diffusion simulations during optimization and can improve computational efficiency. To further enhance diversity and reduce the risk of premature convergence, we introduce a sampling-based strategy that generates new composite solutions by probabilistically selecting best, worst, or random sub-solutions from each group. Multi-objective selection is performed using Pareto dominance, front ranking, an elite archive, and crowding distance. The proposed framework maintains population diversity through subpopulation co-evolution and adaptive sampling. Experimental evaluations conducted on eleven synthetic networks and five real-world datasets show that the proposed method achieves competitive or improved performance relative to strong baselines, considering both fairness of coverage across groups and overall influence propagation.
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area. This paper for the Hot-off-the-Press track at GECCO 2025 summarizes the work C. Bian, Y. Zhou, M. Li, and C. Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. Artificial Intelligence, 2025, 341: 104308. [5]