Jul 2026· IEEE Transactions on Neural Networks and Learning Systems· Vol PP· 0 citations
Medicine
TL;DR
Ensemble-GNAS is proposed, a novel framework that integrates evolutionary GNAS with ensemble learning to address limitations of existing graph NAS algorithms and outperforms the state-of theart methods in both classification accuracy and performance stability.
Abstract
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.
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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.
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Discovering optimal graph neural network (GNN) architectures for various tasks is both labor-intensive and time-consuming. To reduce human effort, graph neural architecture search (GNAS) has recently been utilized to automatically identify effective GNN architectures for specific tasks, achieving competitive or even superior performance compared to manually designed architectures. However, existing GNAS methods fail to identify optimal architectures in the presence of structural and semantic noise, where structural noise refers to missing or redundant edges within the graph structure, and semantic noise denotes inaccurate node representations derived from ambiguous node features such as textual vagueness or semantic ambiguity. In this paper, we address this problem for the first time via theoretical analyses and empirical evaluations. We discover that existing differentiable GNAS methods typically select architectures based on task-relevant information hidden in the graph, being highly sensitive to structural and semantic noise, which results in suboptimal selection of GNN architectures under noise. To handle the structural and semantic noise, we propose Curriculum-GraphLLM, a novel graphLLM framework for joint optimization of architectures, structures, and texts for denoised graph neural architecture search. The core idea is to jointly optimize GNN architectures, graph structures, and textual semantics as a unified denoising process during architecture search. Specifically, we first develop a dynamic topology updating mechanism to adaptively adjust the graph structure. Then, we jointly optimize the GNN architecture and graph structure through a curriculum-based iterative updating approach. To further deal with semantic noise on text-attributed graphs (TAGs), we introduce LLMs as an auxiliary text modeling module to refine textual semantics and guide the co-optimization of text representations, graph structures, and GNN architectures. We conduct extensive experiments to show that our proposed Curriculum-GraphLLM achieves consistently competitive or superior performance compared with existing baselines, especially under structural and semantic noise.
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A self-evolving neural architecture search framework inspired by NCA, in which a population of NCA-based controllers autonomously generates and improves candidate GNN architectures over successive generations, enabling progressive adaptation of the architecture distribution without hand-crafted design rules.
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