This work uses a recently introduced neuro-symbolic framework, to embed a standard GNN into a Relational Bayesian Network (RBN), enabling maximum a-posteriori (MAP) inference that jointly optimises node labels to match the observed local homophily structure of a graph.
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, Li-Xin Cui et al.· Proceedings of the Thirty-Fi...· 0 citations
Graph Neural Networks (GNNs) have exhibited extraordinary performance in data mining. The success depends on the message passing mechanism, which is consistent with the homophily assumption that adjacent nodes share similar behaviors. However, the connected nodes in real-world graphs often show dissimilar behaviors, wh...
Wenjun Wang, Xin Cao, Yawen Li et al.· Proceedings of the 32nd ACM...· 0 citations
Tabular data are central to many real-world applications, yet deep learning models often underperform compared to tree-based methods due to limited relational inductive bias. We propose a unified framework that models each tabular instance as an instance-level statistical feature graph to enable learning with Graph Neu...
Shashank Parmar· 2026 International Conferenc...· 0 citations
A novel Unified Counterfactual Explainer for GNNs that generates counterfactual explanations for both node and graph classification tasks through a unified approach, and offers maximum flexibility by supporting maximum flexibility.
The results suggest that persistent homology offers a useful quantitative lens on the compression--reconstruction tradeoff in PCNs, and that both model capacity and the recurrent, bidirectional dynamics of predictive coding inference shape when this tradeoff is resolved across layers.
It is demonstrated that the information needed to classify heterophilic nodes correctly is often recoverable by retraining the classification head of a model, or even just the final linear classification layer.
Preben M. Ness, Fariz Ikhwantri, D. Marijan· 0 citations
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