Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 32 references
Abstract
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.
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.
Samra Sana, Giorgio Mantica, Saul Imbrici· 0 citations
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.
Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce Graph Autoencoder Uncovering Descriptive Information (GAUDI), an unsupervised graph deep learning framework designed to capture both local details and global structure. GAUDI employs an hourglass architecture with hierarchical pooling and upsampling layers linked through skip connections, which preserve essential connectivity information throughout the encoding–decoding process. Even though identical or highly similar underlying parameters describing a system’s state can lead to significant variability in graph realizations, GAUDI consistently maps them into nearby regions of a structured and continuous latent space, effectively disentangling invariant process-level features from stochastic noise. We demonstrate GAUDI’s versatility across multiple applications, including small-world networks modeling, characterization of protein assemblies from super-resolution microscopy, analysis of collective motion in the Vicsek model, and identification of age-related changes in brain connectivity. Comparison with related approaches highlights GAUDI’s superior performance in analyzing complex graphs, providing new insights into emergent phenomena across diverse scientific domains.
Many real-world systems—ranging from protein structures to financial networks—are naturally represented as labeled graphs, where both topology and node attributes carry critical information. A fundamental question in analyzing such data is whether two graphs (or subgraphs) exhibit statistical dependence, which may indicate shared generative mechanisms or latent interactions. Unlike classical dependence testing, the graph setting introduces unique challenges: dependence can manifest through structural similarity, label correlation, or their interplay, potentially reinforcing or obscuring each other. We propose a novel and practical framework for dependence testing in labeled graphs via mutual information over a structure-weighted joint label distribution. This approach jointly captures topological and attribute-based signals while remaining robust to imperfect or noisy node alignments. We provide theoretical guarantees with explicit error bounds and validate our method on both synthetic and real-world datasets, including protein structures from the lipocalin family, and recurring motifs in the Cora citation network. Our results demonstrate that the proposed test is a statistically sound and an effective tool for uncovering nontrivial dependencies in graph data.
Nikolaos Papagiannis, Vasam Manjveekar Prabantu, A. Grama et al.· Proceedings of the 32nd ACM...· 0 citations
Graph classification plays a central role in many scientific disciplines. While classical kernel-based methods and graph neural networks achieve strong predictive performance, they often require substantial computational resources. Hyperdimensional Computing (HDC) has recently emerged as an efficient and noise-resilient alternative, providing lightweight models that are attractive for resource-constrained settings. Within this context, GraphHD is a representative HDC-based approach for graph classification; however, its encoding process can become costly on large graphs and its standard configuration relies on a single centrality choice (PageRank) for node-to-hypervector assignment.
In this work, we go beyond PageRank in GraphHD by systematically evaluating alternative centrality measures (degree, closeness, betweenness, Katz, and eigenvector) and by introducing two new encoding variants. GraphHD-Level preserves quantitative structural information by mapping centrality values to level-hypervectors, whereas GraphHD-Order simplifies the algorithm by eliminating edge encoding and aggregating node hypervectors directly. Experiments on six widely used benchmarks from cheminformatics and bioinformatics (MUTAG, ENZYMES, PROTEINS, DD, NCI1, and PTC\_FM) 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.