Multidomain Evolutionary Optimization on Adversarial Link Perturbation in Imbalanced-Size Complex Systems
针对规模高度不平衡的复杂网络，提出一种协调多域进化优化方法，通过图粗化策略和双向优化框架实现跨域知识迁移，在对抗性边扰动攻击社区检测任务中优于现有方法。
Real-world complex systems usually share structural characteristics such as the small-world property, power-law distributions, and community structure. Multidomain evolutionary optimization (MDEO) leverages these commonalities to search for optimal solutions in multiple domains simultaneously. However, it still faces challenges in achieving effective cooperation when the networks involved are of highly imbalanced sizes. To address this issue, we propose the harmonized MDEO (HMDEO), in which two graph coarsening strategies are developed to jointly coarsen the large network to a fine level, acting as the bridge between the large network and the small network for knowledge exchange. Following that, we propose a bidirectional optimization framework incorporating two cases: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large-to-small</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">small-to-large</i>, allowing solutions optimized in one domain to be seamlessly transferred to another domain of varied scales. To enhance applicability, we also tailor the measurement of network similarity and the network alignment strategy targeted to imbalanced-size scenarios. The effectiveness of HMDEO is demonstrated through experiments on several pairs of networks of differing scales, where HMDEO outperforms other optimization approaches in addressing adversarial edge perturbation against community detection in complex systems.