ID 原文 译文
25735 本文在前期提出的双正切变换方法( DAT) 的基础上，针对高阶曲面建模中含有 ＲＲ/Ｒ5、Ｒ/Ｒ4和 1 /Ｒ3等形式积分核的近奇异性问题，通过引入指数变换解决了 DAT 算法在近奇异点与源单元非常接近时算法不稳定的问题，并通过引入形函数变换解决了 DAT 近奇异点与源单元边界靠近时积分不稳定的问题，形成改进型双正切变换方法( IDAT) 。 Based on the former research of the Double Arctan-Transformation ( DAT) , the improved Double Arctan-Transformation ( IDAT) is proposed to improve the stability and accuracy of the nearly singular integrals with singular kernel ＲＲ/Ｒ5, Ｒ/Ｒ4and 1 /Ｒ3 for higher order geometry modeling. Specifically, the exponential transformation is utilized to stabilize the integrals when the field points are extremely close to the source surface．
25736 相对于 DAT，所提 IDAT 更稳定高效。 Furthermore, the shape-function transformation is adopted to stabilize the integrals when the projection point approaches to the border of source surface．
25737 所提 IDAT 不仅可用于曲面单元中的高阶近奇异性问题的精确积分，同时也适用于低阶近奇异积分问题。 The proposed IDAT is also effective for the lower orders of the nearly singular integral kernels．
25738 理论分析与数值算例验证了本文所提方法的精确性与稳定性。 With theoretical analysis and typical testing cases, the accuracy and stability performance of IDAT is fairly evaluated．
25739 网络表示学习旨在将网络信息表示为低维稠密的实数向量，解决链接预测、异常检测、推荐系统等任务。 Network representation learning aims to learn the low-dimensional dense real-valued vector of network information, which solves practical tasks such as link prediction, anomaly detection, and recommendation systems．
25740 近年来，网络表示学习研究取得重大进展，但研究多基于静态网络，而真实世界构成的网络是动态变化的，对动态网络分析的需求日益增加。 Ｒecently, network representation learning has made great progress. Most existing researches focus on static networks, while real network is dynamic all the time.
25741 本文总结了当前动态网络表示学习的方法与研究进展，首先提出网络表示学习的动机，阐述动态网络以及表示学习的发展历史与理论基础; This survey proposes state of the arts on representation learning of dynamic network． Firstly, it provides historical overview of representation learning in network, followed by the motivation and theoretical basis of dynamic network representation learning．
25742 接着，系统概述了大量动态网络嵌入方法，包括基于矩阵分解的动态图嵌入、基于随机游走的动态图嵌入、基于深度学习的动态图嵌入和基于重构概率的动态图嵌入，并分析与比较，给出动态网络表示学习的应用场景; Then comprehensive analysis of dynamic models is proposed, including matrix factorization, random-walk, deep learning, edge reconstruction based dynamic models, and gives the application scenarios of dynamic network embedding．
25743 最后，总结未来网络表示学习的研究方向。 Finally, research directions of representation learning in the future are summarized.
25744 只有考虑网络的动态性，才能真实反映现实网络的演化，使网络表示学习更具价值。 Only when considering the temporal dynamics, structure and content can we truly reflect the evolution of the real network and make network representation learning more valuable.