A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks
This provides a generic solution for combinatorial optimization across various domains, though it appears incremental as it builds on existing GNN approaches.
The authors tackled the lack of a unified framework for solving combinatorial optimization problems (COPs) with graph neural networks (GNNs), proposing a framework that includes graph representation, conversion, decomposition, and simplification to address both graph-structured and non-graph-structured COPs.
Graph neural networks (GNNs) have emerged as a powerful tool 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, 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 the relational information and extract features from the graph representation of COPs, offering a generic solution to COPs that can address the limitations of state-of-the-art in solving non-graph-structured and highly complex graph-structured COPs.