Multi-Domain Evolutionary Optimization of Network Structures

2024-06-21

arXiv.org

AI Summary

This study addresses community deception in complex networks—a high-security-risk combinatorial optimization problem—by proposing the Multi-Domain Evolutionary Optimization (MDEO) framework. MDEO leverages cross-domain network commonalities (e.g., power-law degree distribution, small-world property, and modular community structure) to enable knowledge transfer and collaborative optimization. Key contributions include: (1) the first community-aware graph similarity metric coupled with a graph neural network–based network alignment model; and (2) an adaptive cross-domain solution migration mechanism and a mapping-guided mutation operator. Extensive experiments on eight real-world networks spanning diverse domains demonstrate that MDEO significantly outperforms classical evolutionary algorithms in deception effectiveness and solution quality. Furthermore, adversarial attack simulations confirm that MDEO substantially enhances structural robustness of communities and resilience against deception attacks.

Technology Category

• Search and Optimization: Evolutionary Computation
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Multi-Task Evolutionary Optimization (MTEO), an important field focusing on addressing complex problems through optimizing multiple tasks simultaneously, has attracted much attention. While MTEO has been primarily focusing on task similarity, there remains a hugely untapped potential in harnessing the shared characteristics between different domains to enhance evolutionary optimization. For example, real-world complex systems usually share the same characteristics, such as the power-law rule, small-world property, and community structure, thus making it possible to transfer solutions optimized in one system to another to facilitate the optimization. Drawing inspiration from this observation of shared characteristics within complex systems, we set out to extend MTEO to a novel framework - multi-domain evolutionary optimization (MDEO). To examine the performance of the proposed MDEO, we utilize a challenging combinatorial problem of great security concern - community deception in complex networks as the optimization task. To achieve MDEO, we propose a community-based measurement of graph similarity to manage the knowledge transfer among domains. Furthermore, we develop a graph representation-based network alignment model that serves as the conduit for effectively transferring solutions between different domains. Moreover, we devise a self-adaptive mechanism to determine the number of transferred solutions from different domains and introduce a novel mutation operator based on the learned mapping to facilitate the utilization of knowledge from other domains. Experiments on eight real-world networks of different domains demonstrate MDEO superiority in efficacy compared to classical evolutionary optimization. Simulations of attacks on the community validate the effectiveness of the proposed MDEO in safeguarding community security.

Problem

• Optimizing combinatorial problems across multiple network domains
• Transferring knowledge between domains with shared characteristics
• Solving adversarial link perturbation in complex networks

Innovation

• Community-level graph similarity measurement for knowledge transfer
• Graph learning-based network alignment model for solution transfer
• Self-adaptive mechanism with knowledge-guided mutation optimization