Graph Neural Networks for Binary Programming

arXiv:2404.04874v1 Announce Type: new
Abstract: This paper investigates a link between Graph Neural Networks (GNNs) and Binary Programming (BP) problems, laying the groundwork for GNNs to approximate solutions for these computationally challenging problems. By analyzing the sensitivity of BP problems, we are able to frame the solution of BP problems as a heterophilic node classification task. We then propose Binary-Programming GNN (BPGNN), an architecture that integrates graph representation learning techniques with BP-aware features to approximate BP solutions efficiently. Additionally, we …

abstract arxiv binary cs.ai cs.et cs.lg gnns graph graph neural networks networks neural networks node paper programming sensitivity solution solutions type