Jul 2026· International Journal of Uncertainty Fuzziness and Knowledge-Based Systems· 0 citations
TL;DR
The proposed AFGNN model dynamically regulates the fusion of view-specific embeddings, enhances reasoning capability, and suppresses uncertainty during representation learning, and demonstrates the effectiveness and superiority of AFGNN compared with state-of-the-art GNN-based link prediction models.
Abstract
Link prediction has long been regarded as a fundamental problem in networked data analysis and mining, owing to its importance in a wide range of real-world applications, including social network analysis, recommendation systems, and biological network modeling. Over the years, numerous approaches have been proposed to address this problem, ranging from similarity-based heuristics and probabilistic models to dimensionality reduction and deep learning (DL)-based representation learning techniques. These methods aim to capture latent relational patterns among nodes in order to infer missing or potential links. Recently, graph neural networks (GNNs) have emerged as a powerful DL paradigm for graph-structured data representation learning and have demonstrated strong performance in complex link prediction tasks. In particular, multi-view GNN-based approaches have gained increasing attention by integrating different GNN architectures to capture complementary structural perspectives, such as local neighborhood proximity and global graph topology. This strategy enables the learning of richer node embeddings and improves task-driven prediction performance. However, the integration of multiple GNN layers and architectures often introduces feature uncertainty and noise amplification, which can degrade model stability and predictive accuracy. To address these challenges, this paper proposes AFGNN, a novel framework that integrates adaptive neuro-fuzzy inference with multi-view graph neural learning. By leveraging a robust fuzzy neural architecture with an adaptive inference engine, our proposed AFGNN model dynamically regulates the fusion of view-specific embeddings, enhances reasoning capability, and suppresses uncertainty during representation learning. Extensive experiments on benchmark graph datasets demonstrate the effectiveness and superiority of AFGNN compared with state-of-the-art GNN-based link prediction models.
The graph neural networks (GNNs) have been successfully applied to non-Euclidean graph data mining tasks, attracting widespread attention. At present, to achieve promising performance, many researchers use neural architecture search (NAS) on GNNs to enhance applicability and accuracy without requiring significant manual intervention or rich domain knowledge. However, most existing graph NAS (GNAS) algorithms struggle with high-dimensional data and often overlook the characteristics and distribution of different solutions within the search space, which may result in local optima and underutilization of the search potential. This article proposes Ensemble-GNAS, a novel framework that integrates evolutionary GNAS with ensemble learning to address these limitations. Within this framework, we develop two key strategies: an evolutionary GNAS method based on a niche strategy to enhance local search capability and the diversify candidate networks and an ensemble fusion strategy based on a tree-structured Parzen estimator (TPE) to optimize the weights of candidate networks in an ensemble model. The proposed approach is evaluated on: 1) node classification datasets, including citation networks for paper classification and cancer-specific gene interaction networks for driver gene identification and 2) link prediction and graph classification datasets. The experimental results indicate that the proposed approach outperforms the state-of-the-art methods in both classification accuracy and performance stability. Moreover, the ablation study verifies the contribution of the integrated niche and TPE strategies within the overall framework. The source code of Ensemble-GNAS can be obtained from https://github.com/WilfongGuo/EnsembleGNAS.
Weifeng Guo, Pengyu Wang, Ying Bi et al.· IEEE Transactions on Neural...· 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.
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.
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 survey provides a comprehensive overview of OOD detection methods, organizing them into traditional and deep learning families before examining graph-aware GNN-based approaches and synthesizing the conceptual relationships between these paradigms through three working hypotheses: graph-aware transfer, structure-sensitive scoring, and hybrid objective design.
Mao Nguyen, Thien Pham, Long Song Thien Nguyen et al.· IEEE Access· 0 citations
Link prediction describes the procedure for discovering out how likely it is that an edge will be present in a graph network. In the realm of graph network and graph data analytics, link prediction is regarded as a crucial challenge. Estimating possible future linkages among nodes in a network using an overview of the network at a certain moment in time is the focus of link prediction. This technique is utilized in various domains, including recommended systems, network traffic prediction, protein-protein interactions, and numerous other areas. Typically, conventional methods are implemented to quantify the degree of similarity between two specified nodes in a fixed network. The evaluation of networks that undergo dynamic changes over time, however, has recently become the subject of academic research. Heuristic node similarity scores, latent node embeddings, and explicit node attributes are the mainstays of current link prediction methods. Over time, graph neural networks (GNNs) have proven themselves superior to more conventional techniques of link prediction due to their ability to concurrently learn from graph structure and node/edge properties. A graph neural network model for predicting probabilistic links with network embedding via generalized graph convolutional networks, referred to as LPNE2GGCN for graph network datasets, has been proposed. This approach draws inspiration from the promising outcomes achieved by GCNs in evaluating such data. The LPNE2GGCN approach leverages node embedding (Node2Vec) via generalized graph convolutional networks (GraphSAGE). To assess its effectiveness, LPNE2GGCN has been compared against established latent node representation methods, including Node. The performance of LPNE2GGCN is evaluated by contrasting it with well-known techniques for latent node representation. The framework further improves link prediction accuracy by utilizing learning-based optimization techniques like Adadelta and Adam. The experimental results show that the suggested strategy outperforms the state-of-the-art across multiple social network datasets, including those using node embedding techniques and various approaches based on graph neural networks. The proposed model is based on graph neural network techniques that integrates the features obtained from node2vec and utilizes them as input for a hidden neural network consisting of three layers. The initial results suggest that this methodology produces excellent results compared to conventional methods across diverse social network datasets.
Riju Bhattacharya, N. K. Nagwani, Deepak Suresh Asudani et al.· IEEE Access· 0 citations