Results show that mixed-distribution training can improve structural transfer in GNN-based centrality approximation, while identifying closeness centrality's sensitivity to topology as an open challenge.
Abstract
Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centrality, formulated as a node-ranking problem. Exact centrality values are used as supervision, and ranking quality is evaluated using Kendall's tau rank correlation. We study whether message-passing GNNs can learn transferable structural representations across different graph topologies rather than only fitting the distribution used during training. On unseen Erdos renyi graphs, the proposed models achieve tau = 0.851 for betweenness and tau = 0.894 for closeness. A large-scale betweenness model trained on graphs with N = 5,000 nodes achieves tau = 0.938, demonstrating scalability. Mixed-distribution training on Erdos renyi, Barabasi-Albert, and Gaussian Random Partition graphs improves betweenness transfer across graph families. In contrast, closeness centrality remains more sensitive to community-structured graphs and shows reduced transfer to real-world topologies. Finally, GNN inference achieves up to a 97.7x speedup over exact computation. These results show that mixed-distribution training can improve structural transfer in GNN-based centrality approximation, while identifying closeness centrality's sensitivity to topology as an open challenge.
Experiments show that replacing PageRank with alternative centralities yields similar F1-scores while offering notable runtime savings, and that GraphHD-Order remains competitive with the original GraphHD baseline while providing consistent speedups in encoding time.
A diffusion-enhanced inductive link prediction framework that combines Graph Diffusion Convolution (GDC), structural node descriptors, and neighborhood aggregation from GraphSAGE is proposed that achieves higher accuracy than the other models on the benchmark datasets.
A training-free NUI estimation procedure based on clustering consistency with ground-truth labels is introduced, providing a proxy for task-relevant information without supervised learning, and a strong correlation between estimated NUI and downstream classification accuracy is observed, validating NUI as an effective measure of representation utility.
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
This work proposes LIGR (Label Informativeness-Guided Rewiring), which maximizes an information-theoretic measure quantifying how much neighbors’ labels reveal about node labels, suitable for applications requiring interpretability (biological networks, social graphs).
Rucha Bhalchandra Joshi, Subhankar Mishra· Data mining and knowledge di...· 0 citations
This survey organises the field around a single design space, derives the spectral and spatial formulations from shared first principles, and connects expressive power to the Weisfeiler-Leman hierarchy with explicit statements of what current architectures can and cannot separate.