自适应图构建+潜在模式挖掘，提升图嵌入泛化能力
Neighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
- 根据邻域自适应学习图结构，无需预设邻居数
- 通过低秩重构与ℓ₂,₀约束，灵活挖掘多种模式特征
- 适用于社交网络、推荐系统等多场景，尤其擅长复杂结构数据
图嵌入广泛应用于网络分析、社交网络挖掘、推荐系统和生物信息学等领域。然而，现有图构建方法通常需要预先定义邻域大小，限制了数据中潜在结构关联的有效揭示。同时，基于线性投影的图嵌入方法依赖单一模式挖掘，难以适应不同场景。为此，我们提出一种新模型——邻域自适应广义线性图嵌入（NGLGE），基于潜在模式挖掘。该模型引入针对邻域的自适应图学习方法，有效揭示数据内在关联；同时利用重构的低秩表示，并对投影矩阵施加ℓ₂,₀范数约束，实现对额外模式信息的灵活探索。此外，推导出高效的迭代求解算法。在多个不同场景的数据集上进行的对比实验表明，所提模型性能优于当前最优方法。
原文摘要 · Abstract (English)
Graph embedding has been widely applied in areas such as network analysis, social network mining, recommendation systems, and bioinformatics. However, current graph construction methods often require the prior definition of neighborhood size, limiting the effective revelation of potential structural correlations in the data. Additionally, graph embedding methods using linear projection heavily rely on a singular pattern mining approach, resulting in relative weaknesses in adapting to different scenarios. To address these challenges, we propose a novel model, Neighborhood-Adaptive Generalized Linear Graph Embedding (NGLGE), grounded in latent pattern mining. This model introduces an adaptive graph learning method tailored to the neighborhood, effectively revealing intrinsic data correlations. Simultaneously, leveraging a reconstructed low-rank representation and imposing $\ell_{2,0}$ norm constraint on the projection matrix allows for flexible exploration of additional pattern information. Besides, an efficient iterative solving algorithm is derived for the proposed model. Comparative evaluations on datasets from diverse scenarios demonstrate the superior performance of our model compared to state-of-the-art methods.
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