论文标题
使用网络嵌入技术识别化学动力学系统的过渡状态
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques
论文作者
论文摘要
使用随机步行采样方法进行网络上的特征学习，我们开发了一种为有向图生成低维节点嵌入的方法，并识别随机化学反应系统的过渡态。我们修改了现有的基于随机步行的网络嵌入方法中采用的目标功能，以处理有针对性的图形和不同程度的邻居。通过通过梯度上升进行优化，我们将加权图顶点嵌入到低维矢量空间RD中，同时保留每个节点的邻域。然后，我们通过几个识别化学反应的过渡状态，尤其是对于熵系统，证明了该方法对降低尺寸的有效性。
Using random walk sampling methods for feature learning on networks, we develop a method for generating low-dimensional node embeddings for directed graphs and identifying transition states of stochastic chemical reacting systems. We modified objective functions adopted in existing random walk based network embedding methods to handle directed graphs and neighbors of different degrees. Through optimization via gradient ascent, we embed the weighted graph vertices into a low-dimensional vector space Rd while preserving the neighborhood of each node. We then demonstrate the effectiveness of the method on dimension reduction through several examples regarding identification of transition states of chemical reactions, especially for entropic systems.
