{"type":"ScholarlyArticle","title":"ADC-GNN: Adaptive Dual-level Collaborative Graph Neural Networks for Graph Classification","authors":[{"name":"Wan Tang","slug":"wan-tang","given":"Wan","family":"Tang","affiliation":""},{"name":"Lu Bai","slug":"lu-bai","given":"Lu","family":"Bai","affiliation":""},{"name":"Lixin Cui","slug":"lixin-cui","given":"Lixin","family":"Cui","affiliation":""},{"name":"Ming Li","slug":"ming-li","given":"Ming","family":"Li","affiliation":""},{"name":"Hangyuan Du","slug":"hangyuan-du","given":"Hangyuan","family":"Du","affiliation":""},{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":""}],"abstract":"Most existing Graph Neural Networks (GNNs) rely on the node-level message passing or attention mechanisms to propagate and extract useful information. Although recent advances attempt to move beyond purely the node-level propagation by constructing high-level representations, these approaches are often constrained by pre-computed substructures or unidirectional bottom-up aggregations. Consequently, high-level structural semantics cannot effectively feed back to guide node representation learning, limiting the collaborative optimization between fine-grained features and macroscopic structural semantics. To address these limitations, we propose a novel Adaptive Dual-level Collaborative GNN (ADC-GNN) associated with an adaptive dual-level collaborative mechanism. We commence by introducing a set of global, learnable latent prototypes as high-level semantic references, and then employ a relaxed Sinkhorn algorithm to establish differentiable, non-collapsing assignments between nodes and prototypes. Based on these assignments, the ADC-GNN constructs high-level representations and enables interactions among them. We show that the ADC-GNN can inject the learned high-level information back into the node level, forming a closed-loop, bidirectional optimization process. Experiments demonstrate the superior performance of the proposed ADC-GNN on graph classification.","doi":"10.24963/ijcai.2026/337","url":"https://ijcai.org/proceedings/2026/337","pdf":"https://ijcai.org/proceedings/2026/0337.pdf","markdown":"https://ijcai.org/proceedings/2026/337.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/337","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":337,"pages":{"first":3033,"last":3042},"keywords":["Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}