带有协变量平滑的网络布局算法
Network Layout Algorithm with Covariate Smoothing
AI总结：
针对传统网络可视化忽略观测误差与节点属性的问题，提出基于协变量平滑改进Fruchterman-Reingold算法的布局方法，通过统计模型估计边连接概率并平衡观测与估计边，实现更稳健的网络可视化。
摘要：
网络科学研究探索对象之间复杂的连接，应用于社交互动、欺诈检测和疾病传播等多个领域。网络可视化有助于概念化研究问题并形成科学假设。网络作为数学上的高维对象，需要降维以进行（平面）可视化。经验网络的可视化面临额外挑战。它们通常包含假阳性（虚假）和假阴性（缺失）的边。传统可视化方法未考虑观测中的误差，可能导致解释产生偏差。此外，当代网络数据包含丰富的节点属性。然而，传统方法在计算节点位置时忽略了这些属性。我们的可视化方法旨在利用节点属性的丰富性来弥补网络数据的局限性。我们采用一个统计模型，基于节点的协变量来估计节点之间边连接的概率。我们改进了Fruchterman-Reingold算法，以纳入估计的二项连接概率，使从业者能够平衡对观测边与估计边的依赖。我们探索了最优的平滑水平，提供了一种在布局中包含相关节点信息的自然方式。结果证明了我们的方法在实现稳健网络可视化方面的有效性，为改进分析提供了见解。
英文摘要：
Network science explores intricate connections among objects, employed in diverse domains like social interactions, fraud detection, and disease spread. Visualization of networks facilitates conceptualizing research questions and forming scientific hypotheses. Networks, as mathematical high-dimensional objects, require dimensionality reduction for (planar) visualization. Visualizing empirical networks present additional challenges. They often contain false positive (spurious) and false negative (missing) edges. Traditional visualization methods don't account for errors in observation, potentially biasing interpretations. Moreover, contemporary network data includes rich nodal attributes. However, traditional methods neglect these attributes when computing node locations. Our visualization approach aims to leverage nodal attribute richness to compensate for network data limitations. We employ a statistical model estimating the probability of edge connections between nodes based on their covariates. We enhance the Fruchterman-Reingold algorithm to incorporate estimated dyad connection probabilities, allowing practitioners to balance reliance on observed versus estimated edges. We explore optimal smoothing levels, offering a natural way to include relevant nodal information in layouts. Results demonstrate the effectiveness of our method in achieving robust network visualization, providing insights for improved analysis.
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。
