GCoDE paper accepted by IEEE TC

👏 Paper title: GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search. We propose GCoDE, an architecture-mapping co-search framework for efficient device-edge GNN co-inference. It jointly explores GNN architectures and deployment mappings, balancing communication and computation across device-edge systems. The framework models how graph partitions, neural architectures, and system placement interact, so the search can avoid designs that are accurate but communication-heavy or efficient but accuracy-limited. By co-optimizing the model architecture and system mapping, GCoDE improves inference efficiency while maintaining task performance for device-edge GNN deployment.