GCN拉普拉斯算子的低秩投影
Low-Rank Projections of GCNs Laplacian
摘要：
在这项工作中，我们研究的行为标准模型的社区发现下的光谱操作。通过各种消融实验，我们评估带通滤波对GCN性能的影响：我们凭经验表明，节点分类的大部分必要和使用的信息都包含在低频域中，因此与图像相反，高频对社区发现不那么重要。特别是，它有时是可能的，以获得一个国家的最先进的水平与简单的分类器，只依赖于一些低频率的准确性。
英文摘要：
In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the performance of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to images, high frequencies are less crucial to community detection. In particular, it is sometimes possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
