Optimization on Graphs: AI and Distributed Approaches

Chair: Dr. Rolando Menchaca-Méndez

Optimization problems defined over graphs are ubiquitous in modern science and engineering, serving as the foundation of solutions in areas as diverse as telecommunications, logistics, social network analysis, computational biology, and machine learning. The increasing scale and dynamic nature of real-world graph data present significant challenges and exciting opportunities for novel theoretical and practical advancements.

This special session, "Optimization on graphs: AI and Distributed Approaches," aims to bring together researchers and practitioners to explore cutting-edge developments in optimization techniques applied to graph structures. We are particularly interested in original contributions that leverage modern methodologies, including all facets of Artificial Intelligence (AI), to address complex graph optimization challenges.

We invite submissions presenting theoretical insights, novel algorithms, and practical applications in graph optimization.

Topics of interested include (but are not limited to):

  • AI-Enhanced Optimization on Graphs: Machine Learning for graph optimization (e.g., Graph Neural Networks, Reinforcement Learning, Deep Learning approaches).
  • Evolutionary Computation and Swarm Intelligence for Graph Problems.
  • Heuristic and Metaheuristic algorithms informed by AI for graph optimization.
  • Mathematical programming solutions for graph optimization.
  • Explainable AI in graph optimization.
  • Distributed Optimization on Graphs: Decentralized algorithms for large-scale graph problems.
  • Optimization for Very Large Graphs: Scalable algorithms for massive graph datasets.
  • Approximation algorithms for NP-hard graph problems.
  • Techniques for handling dynamic and streaming graphs.
  • Continual Graph Learning and Optimization: Algorithms for adapting optimization solutions to evolving graph structures.
  • Online learning and optimization on graphs.
  • Applications of Graph Optimization in:
    • Supply chain and logistics.
    • Telecommunication networks.
    • Bioinformatics and healthcare.
    • Transportation and urban planning.
    • Smart cities.
We welcome both theoretical contributions presenting rigorous analysis and empirical studies demonstrating the effectiveness of proposed methods on real-world or synthetic datasets. Submissions should highlight the novelty of the proposed approach and its potential impact on graph optimization.

This special session is closely related to the Special Issue "Advanced Algorithm Theory and Computation for Complex Networks" in the journal Mathematics.

Rolando Menchaca-Méndez is a Professor of the Network and Data Science Laboratory at the Computer Research Center of the Mexican National Polytechnic Institute. He received his Ph.D. in Computer Engineering from the University of California at Santa Cruz in 2009. His current research interests include combinatorial optimization, reinforcement learning, cloud computing, computer networks, and the IoT.