Sep 2026· Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence· 0 citations· 44 references
TL;DR
A Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of-Distribution detection (termed HGOOD), which constructs two branches to hierarchically mine graph compact semantics in a comprehensive manner and introduces a cross-branch prototype contrast that aligns the captured graph patterns with their cross-branch clustering prototypes to enhance the semantic manifold of the in-distribution graph.
Abstract
With the increasing application of graph learning advanced by deep learning, out-of-distribution (OOD) detection for graph-structured data has become an imperative challenge in the real world. Graph neural networks (GNNs) provide a promising solution for OOD detection. However, GNNs' core message-passing mechanism inherently relies on local neighborhood aggregation, and traditional graph structures only characterize pairwise node relations, failing to capture high-order associations and global patterns. Towards this end, in this paper, we propose a Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of Distribution detection (termed HGOOD). Specifically, we construct two branches to mine graph semantics in a comprehensive manner. On the one hand, we employ a graph feature branch to encode neighborhood interactions via node-level and graph-level contrastive learning. On the other hand, we incorporate the hypergraph-global branch, which adaptively models graphs’ high-order global correlations. Building upon this, we introduce a cross-branch prototype contrast that aligns the captured graph patterns with their cross-branch clustering prototypes to enhance the semantic manifold of the in-distribution (ID) graph. Extensive experiments on benchmark datasets demonstrate that our HGOOD consistently outperforms prior methods. Our code is available at https://anonymous.4open.science/r/HGOOD-CFE2/.
This work proposes a novel Adaptive Dual-level Collaborative GNN associated with an adaptive dual-level collaborative mechanism, and shows that the ADC-GNN can inject the learned high-level information back into the node level, forming a closed-loop, bidirectional optimization process.
Wan Tang, Lu Bai, Lixin Cui et al.· Proceedings of the Thirty-Fi...· 0 citations
A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.
Mo Li, Zhaosong Zhao, Linlin Ding et al.· Annual International ACM SIG...· 0 citations
A combined HGT-based framework incorporating contrastive representation learning and deep clustering with multi-round training via pseudo-labels is presented, enabling the model to better deal with label scarcity, heterogeneous dependencies, and overlapping semantics in practical, complex, attributed networks.
Hamza Haddad, Hicham Attariuas, A. Younes· Edelweiss Applied Science an...· 0 citations
DeltaGNN is introduced, to the best of the authors' knowledge, among the first scalable (featuring linear computational and memory complexity overhead) and generalizable (capable of effectively handling graphs with diverse homophily, density, and topology) architectures for long-range and short-range interaction detection.
Kevin Mancini, Islem Rekik· IEEE Transactions on Pattern...· 2 citations
Similarity-guided Structural Matching Learning for Graph Dataset Condensation (SSGDC) is proposed, which efficiently reduces repository size while maintaining both task performance and structural information.
Yi-Yang Zhang, Yutong Ye, Ying-Bo Zhou et al.· Proceedings of the Thirty-Fi...· 0 citations
Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with the number of nonlinear transformations applied. This coupling can make deep architectures difficult to optimize and can lead to over-smoothing, over-squashing, and the loss of long-range information. Linearized Graph Sequence Models (LGSMs) address this issue by separating information depth from processing depth and treating the successive propagation states of each node as a sequence. However, existing LGSMs construct these sequences using fixed graph operators, limiting their ability to adapt propagation to the input graph, node features, and downstream task. We introduce HOPPER, an end-to-end learnable extension of LGSM that learns how hop sequences should be extracted before they are processed by a modern state-space model. Our framework supports feature-conditioned, structure-aware, graph- and hop-adaptive propagation mechanisms while preserving permutation equivariance. Standard adjacency-based and non-backtracking LGSM sequences arise as special cases of our proposed extractor family. We show that HOPPER is state-of-the-art or competitive across the ECHO-Synth benchmark, and that varying the maximum neighborhood size of message backtracking cancellation (i.e. structural memory window) can optimize accuracy on the LRIM physics-based long-range dependency benchmark. These results demonstrate that learnable sequence extraction provides a flexible and effective approach to long-range graph representation learning.
