提出抗噪半监督图自编码器，提升复杂网络中重叠社区检测准确率。
A Noise-Resilient Semi-Supervised Graph Autoencoder for Overlapping Semantic Community Detection
- 融合结构、属性与先验知识，通过多头注意力学习语义表示
- 在60%特征被破坏时仍保持稳定性能，NMI和F1-score均优于现有方法
- 适合处理含噪声的真实网络数据，尤其关注重叠社区发现
具有重叠结构的网络社区检测在真实世界环境中面临显著挑战，尤其是在存在噪声的情况下，整合拓扑结构、节点属性和先验信息至关重要。为此，我们提出一种半监督图自编码器，结合图多头注意力与模块度最大化，以鲁棒地检测重叠社区。该模型通过融合结构、属性及先验知识来学习语义表示，并显式处理节点特征中的噪声。关键创新包括抗噪架构与面向社区质量优化的语义半监督设计，通过模块度约束实现。实验表明，该模型在重叠社区检测上优于现有最先进方法（在NMI和F1-score上均有提升），并在60%特征被破坏时表现出极强鲁棒性。结果凸显了在复杂网络中整合属性语义与结构模式对精准社区发现的重要性。
原文摘要 · Abstract (English)
Community detection in networks with overlapping structures remains a significant challenge, particularly in noisy real-world environments where integrating topology, node attributes, and prior information is critical. To address this, we propose a semi-supervised graph autoencoder that combines graph multi-head attention and modularity maximization to robustly detect overlapping communities. The model learns semantic representations by fusing structural, attribute, and prior knowledge while explicitly addressing noise in node features. Key innovations include a noise-resistant architecture and a semantic semi-supervised design optimized for community quality through modularity constraints. Experiments demonstrate superior performance the model outperforms state-of-the-art methods in overlapping community detection (improvements in NMI and F1-score) and exhibits exceptional robustness to attribute noise, maintaining stable performance under 60\% feature corruption. These results highlight the importance of integrating attribute semantics and structural patterns for accurate community discovery in complex networks.
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