Engineering· 2026Q1
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks
- 1citations
- Q1SCImago
- 2026year
Short summary
A new unified framework based on Graph Neural Networks (GNNs) can solve a wide range of combinatorial optimization problems (COPs), including those not inherently graph-structured, overcoming limitations of current methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a unified framework for solving combinatorial optimization problems (COPs) using Graph Neural Networks (GNNs).
- Includes methods for representing COPs as graphs, converting non-graph COPs to graph structures, and simplifying graphs.
- Leverages GNNs' feature extraction and relational information capabilities.
- Addresses limitations in solving non-graph-structured and highly complex graph-structured COPs.
AI-generated from the title and abstract; the full text is not read.
Abstract
Graph neural networks (GNNs) have emerged as powerful tools for solving combinatorial optimization problems (COPs), exhibiting state-of-the-art performance in both graph-structured and non-graph-structured domains. However, existing approaches lack a unified framework capable of addressing a wide range of COPs. After presenting a summary of representative COPs and a brief review of recent advancements in GNNs for solving COPs, this paper proposes a unified framework for solving COPs based on GNNs, including graph representation of COPs, the equivalent conversion of non-graph-structured COPs to graph-structured COPs, graph decomposition, and graph simplification. The proposed framework leverages the ability of GNNs to effectively capture relational information and extract features from graph representations of COPs, offering a generic solution to COPs that can address the limitations of the state of the art in solving non-graph-structured and highly complex graph-structured COPs.
The authors' abstract, as published at the source. Engineering, 2026 · DOI ↗
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Field: Control and Systems Engineering
Control and Systems EngineeringEngineering