Artificial Intelligence for Complex Network: Potential, Methodology and Application

2024-02-23

arXiv.org

AI Summary

Complex networks face core challenges in real-world modeling and deployment, including unclear mechanisms governing the emergence of macroscopic order from microscopic disorder, difficulties in fusing multi-source heterogeneous data, and weak capabilities for dynamic causal inference. This paper presents the first systematic interdisciplinary survey of AI-enhanced complex network research and proposes a novel tripartite paradigm: “structural inference—dynamic prediction—causal modeling.” Integrating graph neural networks, self-supervised representation learning, spatiotemporal sequence modeling, and explainable AI, we establish a unified methodological framework. Extensive experiments across seven real-world domains—including social, biological, and transportation networks—demonstrate significant improvements in link prediction, community evolution tracking, and anomalous diffusion identification. Our framework provides a scalable, interpretable, and generalizable AI infrastructure for data-driven modeling in statistical mechanics, topology, and network dynamics.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field.

Problem

• Exploring AI's potential to overcome complex network research challenges
• Summarizing key research problems in AI for complex networks
• Reviewing AI methodologies and applications in complex network science

Innovation

• AI enhances complex network analysis
• Summarizes key research problems
• Reviews methodologies and applications