AI 中文总结
该研究针对有向图协方差结构的统计检验问题，提出基于图信号处理的代理生成方案，可检测不规则节点协方差，其性能优于传统对称化图方案，在真实社交网络图上验证了有效性。
AI 中文摘要
非参数统计检验基于代理数据生成，该方法会随机化经验数据中选定的特征。在图的场景下，图信号处理（GSP）提出了多种方案，例如保留由狄利克雷能量衡量的图信号平滑度。然而，如何处理有向图仍是研究的活跃领域。我们首先重新审视有向图广义平稳性的定义，代理信号在平稳性假设下保留协方差。我们证明了该方案检测不规则节点协方差的可行性，并使用对称化图将我们的方法与传统方案进行基准测试。我们还研究了不对称程度如何影响检测性能，从而评估所提出方法的优势。最后，我们展示了从Freeman EIES社交网络数据集中提取的真实世界图的结果。
英文摘要
Non-parametric statistical testing is based on surrogate data generation that randomizes chosen features in the empirical data. In the graph setting, graph signal processing (GSP) brings forward versatile schemes; e.g., to preserve smoothness of graph signals as measured by the Dirichlet energy. However, how to deal with directed graphs remains an active area of research. We begin by revisiting the definition of directed graph wide-sense stationarity. The surrogate signals preserve covariance under the stationary assumption. We demonstrate the feasibility of the scheme to detect irregular node covariance and benchmark our method against conventional schemes using the symmetrized graph. We also investigate how the level of asymmetry affects the detection performance, thus assessing the advantages of the presented approach. Finally, we show results for a real-world graph extracted from the Freeman EIES social network dataset.
CommentsAccepted at EUSIPCO 2026
