Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 6068-6079· 0 citations· 23 references
Abstract
Graph clustering aims to group nodes into meaningful clusters, a crucial task for understanding the behavior of complex networks and their underlying structural organization. Leveraging the expressive power of deep learning, graph neural networks (GNNs) have emerged as popular tools for performing graph clustering with high accuracy. However, despite their promise, existing GNN-based approaches often lack structural reasoning mechanisms to adaptively infer and optimize each node's receptive field in a task-specific manner, limiting their potential to further improve both accuracy and interpretability. In this paper, we propose a novel reinforced structural reasoning framework, termed RGIGC, for interpretable graph clustering. RGIGC formulates receptive field configuration as a structural reasoning problem and employs reinforcement learning with two Q-learning agents to infer and optimize the structural receptive field of each node. These agents are guided by reward criteria that strategically shape the clustering process to achieve desired outcomes. By identifying critical structures of receptive fields that capture the intrinsic features of nodes, RGIGC can enhance the interpretability of the resulting clusters. Extensive experiments on eight real-world graph datasets of varying scales have been conducted, and the results demonstrate that RGIGC outperforms state-of-the-art graph clustering algorithms in both accuracy and interpretability.
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.
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.
Phu Pham· International Journal of Unc...· 0 citations
This work uses Graph Neural Networks (GNNs) to solve Inductive Correlation Clustering, a novel generalization of the CC problem designed to handle unseen graph instances, and indicates that the method serves as an efficient pooling layer, enhancing the ability of GNNs to capture hierarchical structural information in networks.
Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan et al.· 0 citations
Similarity-guided Structural Matching Learning for Graph Dataset Condensation (SSGDC) is proposed, which efficiently reduces repository size while maintaining both task performance and structural information.
Yiyang Zhang, Yutong Ye, Yingbo Zhou et al.· 0 citations
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.
Xin Wang, Haibo Chen, Linxin Xiao et al.· IEEE Transactions on Pattern...· 0 citations
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