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Artificial Intelligence for Complex Network: Potential, Methodology and Application (2402.16887v1)

Published 23 Feb 2024 in cs.SI, cs.AI, cs.LG, and physics.soc-ph

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 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.

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Authors (11)
  1. Jingtao Ding (50 papers)
  2. Chang Liu (867 papers)
  3. Yu Zheng (198 papers)
  4. Yunke Zhang (18 papers)
  5. Zihan Yu (10 papers)
  6. Ruikun Li (11 papers)
  7. Hongyi Chen (23 papers)
  8. Jinghua Piao (12 papers)
  9. Huandong Wang (35 papers)
  10. Jiazhen Liu (21 papers)
  11. Yong Li (630 papers)
Citations (6)
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Summary

  • The paper introduces AI-driven methodologies like graph neural networks and deep generative models to address high-dimensional challenges in network science.
  • It presents a novel taxonomy that categorizes key research problems, bridging theoretical insights with practical applications.
  • The survey highlights future directions where AI not only enhances network modeling but also inspires the formulation of new, interpretable scientific hypotheses.

Harnessing AI in Complex Network Science: A Comprehensive Survey

Introduction to Integrating AI with Complex Networks

Complex networks serve as abstract representations of various real-world systems, encompassing everything from biological ecosystems to intricate human interactions. Despite significant strides in understanding their statistical mechanics, structures, and dynamics, challenges persist in unraveling more realistic systems and enhancing practical applications. The advent of AI technologies, combined with an abundance of network data, ushers in a transformative era for complex network research. This comprehensive survey explores the fusion of AI with complex network science, detailing potential advantages, pivotal research issues, methodologies, and applications. Through this synthesis, we aim to spark further research and progress within this interdisciplinary domain.

Challenges in Complex Network Research

Addressing the intricate dynamics of complex networks presents substantial challenges. These include characterizing higher-order topological properties, elucidating the dynamic mechanisms within networks, modeling the coevolution of node states and their interactions, and optimizing high-dimensional networks. Traditional approaches often fall short due to simplified assumptions or prohibitive computational costs.

AI as a Solution

AI technology, especially deep learning methods, offers new vistas for complex network research. By adeptly managing high-dimensional data and enabling efficient inference without stringent assumptions, AI holds promise for overcoming existing hurdles. Specific AI methodologies such as graph embedding, graph neural networks, dynamic graph learning, and deep generative models showcase significant potential in tackling unresolved issues in complex network science.

Methodological Innovations

The survey introduces a novel taxonomy of research problems in complex network science, discussing the development of AI methodologies in representation, prediction, simulation, inference, generation, and control of complex networks. It underscores AI-enhanced complex network models' practical applications across domains like ecology and biology to urban and social networks, summarizing dataset resources essential for accelerating data-centric studies.

Future Directions

As complex network science progresses, the interplay between theoretical underpinnings and AI-driven approaches becomes crucial. Combining AI with mechanistic models could yield more interpretable, generalized solutions. Conversely, the principles of complex networks could inform the development of more sophisticated AI models, particularly in understanding and designing LLMs and other emergent AI systems. The survey speculates on the transformative capability of AI in discovering new network science theories, emphasizing the need for generating scientifically viable hypotheses.

Integrating AI for Network Science Discovery

In an era brimming with data and computational tools, integrating AI within complex network research opens up unprecedented opportunities for discovery and application. By addressing current limitations and exploring novel AI methodologies, future research can untangle the complexities of real-world networks, contributing to a deeper understanding and potentially revolutionary advances across multiple fields.

Conclusion

This survey not only charts the prescient collaboration between AI and complex network science but also inspires continued exploration into their confluence. It paints a picture of an evolving landscape where AI not merely aids in the analysis of complex networks but becomes instrumental in forging new pathways for understanding and manipulating the fabric of interconnected systems.