标题： LOHA：低通和高通视图之间的直接图谱对比学习 显示英文标题

标题： LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views

摘要： 谱图神经网络能够有效处理具有不同同质性水平的图，低通滤波器挖掘特征平滑性，高通滤波器捕捉差异。 当这些不同的滤波器可以自然形成自监督学习的两个相反视角时，同一节点对应部分之间的共性仍未被探索，导致性能不佳。 本文提出了一种简单而有效的自监督对比框架LOHA，以解决这一差距。 LOHA通过接受“多样性中的和谐”来最优利用低通和高通视角。 而不是仅仅最大化这些不同视角之间的差异，这可能导致特征分离，LOHA通过将来自这两个视角的图信号传播视为复合特征来协调多样性。 具体而言，提出了一种名为谱信号趋势的高维特征作为复合特征的基础，该特征在改变滤波器时相对不受影响，并且仅关注原始特征的差异。 LOHA在9个具有不同同质性水平的真实数据集上，相对于第二名模型平均性能提高了2.8%。 值得注意的是，LOHA在某些数据集上甚至超过了完全监督模型，这突显了LOHA在提高谱GNN对多样化图结构的有效性方面的潜力。 显示英文摘要

摘要： Spectral Graph Neural Networks effectively handle graphs with different homophily levels, with low-pass filter mining feature smoothness and high-pass filter capturing differences. When these distinct filters could naturally form two opposite views for self-supervised learning, the commonalities between the counterparts for the same node remain unexplored, leading to suboptimal performance. In this paper, a simple yet effective self-supervised contrastive framework, LOHA, is proposed to address this gap. LOHA optimally leverages low-pass and high-pass views by embracing "harmony in diversity". Rather than solely maximizing the difference between these distinct views, which may lead to feature separation, LOHA harmonizes the diversity by treating the propagation of graph signals from both views as a composite feature. Specifically, a novel high-dimensional feature named spectral signal trend is proposed to serve as the basis for the composite feature, which remains relatively unaffected by changing filters and focuses solely on original feature differences. LOHA achieves an average performance improvement of 2.8% over runner-up models on 9 real-world datasets with varying homophily levels. Notably, LOHA even surpasses fully-supervised models on several datasets, which underscores the potential of LOHA in advancing the efficacy of spectral GNNs for diverse graph structures.