Aug 2026· CLEI Electronic Journal· 0 citations· 23 references
TL;DR
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.
Abstract
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.
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.
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 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.
Comparing graphs for structural similarity is one of the most important problems in graph analytics. However, due to the nonlinear nature of graphs, this problem is not straightforward to solve. Most existing graph comparison methods either lack expressiveness, do not provide interpretable measures of similarity, or incur high computational costs, limiting their applicability to large graphs. In this article, we propose novel graph kernels based on quantum Rényi $\alpha $ -entropies of different orders, computed from both the unnormalized and normalized Laplacian matrices. We investigate the properties of these entropies and show that they are determined by the frequencies and degree statistics of substructures of different types and sizes, such as simple paths and cycles ofdifferentlengths. By utilizing quantum Rényi $\alpha $ -entropies of different orders, our approach defines efficient, theoretically grounded, and interpretable graph kernels capable of characterizing the structure of unlabeled graphs. Through extensive experiments on benchmark datasets, we demonstrate that our methods achieve competitive or superior performance compared with state-of-the-art techniques, including deep learning approaches, while remaining computationally efficient.
Furqan Aziz· IEEE Transactions on Neural...· 0 citations
Directed hypergraphs have gained increasing attention for modeling group interactions while preserving directionality. However, link prediction in directed hypergraphs has rarely been studied despite its practical significance in complex systems analysis. Existing models perform poorly due to three major challenges in directed hypergraphs: 1) lacking effective feature initialization methods; 2) neglecting to detect higher order substructures; and 3) failing to capture long-range dependencies among vertices. To address these challenges, we propose a novel directed hypergraph motif-based neural network (DHMNN) for directed hyperlink prediction, which simultaneously captures higher order structural and connectivity information from the directed hypergraph topology. First, we introduce directed hypergraph motifs (DH-motifs) to explore higher order neighborhoods, analyzing vertex structural equivalence and generating structural features. Secondly, we utilize hypergraph incidence matrices to measure local connectivity, quantifying vertex co-occurrences and producing connectivity features. Then, we employ hypergraph attention to refine the vertex features at both global and local levels, further capturing long- and short-range dependencies. Finally, a new scoring layer is designed to assess the reliability of each link, considering its local properties, feature variance, and directionality. Extensive experiments on seven metabolic networks and three social networks demonstrate that DHMNN significantly and consistently outperforms state-of-the-art models, achieving a 3.40%-9.90% increase in accuracy. Our code is available at: https://github.com/XihangMeng/DHMNN.
Xihang Meng, Hao Peng, Guangjie Zeng et al.· IEEE Transactions on Neural...· 0 citations