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.
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, which is presented as the heterophily problem. A series of works proposed various models to solve the heterophily problem. They usually assume that the neighborhood distribution of nodes with the same class tends to be similar, while distinguishable from nodes of different classes. However, our experiments discovered that many nodes belonging to different classes share the same neighbor distribution in the mainstream datasets. The performance of GNN also degrades in these conflicting nodes. To detect the conflicting nodes and improve their prediction accuracy, we propose the Conflicting Node Discrimination GNN (CNDGNN). It firstly detects the possible conflicting nodes with the same neighbor distribution according to the selected high-quality pseudo-label. Then it estimates the class type of conflicting nodes with the same neighbor distribution through nodes' high-order neighbor distributions and original features, and the aggregation weights are learned to assign positive weights for nodes of the same class type and negative weights for nodes of different classes. The theoretical analysis proves that CNDGNN performs better than GCN in the conflicting nodes and explores the key design view when solving the conflicting node problem. The experiments demonstrate the effectiveness of our method in the mainstream datasets and verify that our method performs better in conflicting nodes.
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 Neural Networks (GNNs). In this representation, features are treated as nodes, and edges encode pairwise statistical relationships derived from Z-score similarity, covariance, Pearson correlation, or Euclidean distance. This formulation explicitly captures inter-feature dependencies rather than relying solely on implicitly learned interactions. We evaluate the framework across four classification and four regression datasets spanning diverse domains and feature dimensionalities. Results demonstrate competitive and, in several cases, superior performance compared to strong baselines including Random Forests, XGBoost, and multilayer perceptrons. Correlation- and covariance-based edge constructions consistently provide robust performance across tasks. Furthermore, empirical analysis indicates that performance gains become more pronounced in higher-dimensional datasets, suggesting that graph-based representations are particularly effective at modeling complex feature interactions. Overall, this work provides a systematic examination of statistical graph construction for tabular learning and highlights the potential of GNNs as a structured alternative to conventional tabular models.
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.
We study the topology of learned representations in predictive coding networks (PCNs), a neuro-inspired bidirectional architecture, using a quantitative layer-wise persistent homology analysis. We train well-performing PCNs on a synthetic classification dataset ($\geq 99.9\%$ test accuracy) and on MNIST ($\geq 95\%$ test accuracy), and measure how topological features change across layers for different architectures and activation functions. We find that smaller PCNs collapse connected components across layers earlier than larger models (Spearman $\unicode{x1D70C} \in [0.72, 0.79]$ across activations), with model size measured as the sum of hidden-layer widths. We also observe a strong negative correlation ($\unicode{x1D70C} = -0.58$) between the depth at which simplification occurs and reconstruction error; i.e., architectures that simplify later reconstruct better. Finally, a seed-level bootstrap comparison across architectures and activations shows that PCNs consistently collapse connected components later than matched MLPs, with an average difference of $3.6$ layers. These 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.
Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare? Some existing work frames classification of such nodes as a subgroup generalisation problem, where a model performs well on the majority group at the expense of the rare group. Others explain this as a problem of neighbourhood aggregation in graph neural networks (GNNs). We assess these two viewpoints through a detailed evaluation of six GNNs on five datasets of varying homophily, and find that homophilic nodes tend to be easier to classify, even when they are rare---challenging the subgroup framing. However, our findings also nuance existing beliefs about how GNNs misrepresent heterophilic nodes. We demonstrate 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
