An Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problem

TL;DR

This paper introduces a hybrid approach combining graph representation learning with metaheuristics to improve solutions for the constrained incremental graph drawing problem, showing superior performance over existing heuristics.

Contribution

It presents a novel hybrid method, Graph Learning GRASP (GL-GRASP), integrating graph representation learning into the GRASP heuristic for hierarchical graph visualization.

Findings

Deep learning-based node embeddings outperform other techniques.

GL-GRASP heuristics achieve better solution quality than state-of-the-art methods.

The approach scales well to denser graph instances under fixed time constraints.

Abstract

Hybridizing machine learning techniques with metaheuristics has attracted significant attention in recent years. Many attempts employ supervised or reinforcement learning to support the decision-making of heuristic methods. However, in some cases, these techniques are deemed too time-consuming and not competitive with hand-crafted heuristics. This paper proposes a hybridization between metaheuristics and a less expensive learning strategy to extract the latent structure of graphs, known as Graph Representation Learning (GRL). For such, we approach the Constrained Incremental Graph Drawing Problem (C-IGDP), a hierarchical graph visualization problem. There is limited literature on methods for this problem, for which Greedy Randomized Search Procedures (GRASP) heuristics have shown promising results. In line with this, this paper investigates the gains of incorporating GRL into the construction phase of GRASP, which we refer to as Graph Learning GRASP (GL-GRASP). In computational experiments, we first analyze the results achieved considering different node embedding techniques, where deep learning-based strategies stood out. The evaluation considered the primal integral measure that assesses the quality of the solutions according to the required time for such. According to this measure, the best GL-GRASP heuristics demonstrated superior performance than state-of-the-art literature GRASP heuristics for the problem. A scalability test on newly generated denser instances under a fixed time limit further confirmed the robustness of the GL-GRASP heuristics.