Dynamic graph embeddings, inductive and incremental learning facilitate predictive tasks such as node classification and link prediction. However, predicting the structure of a graph at a future time step from a time series of graphs, allowing for new nodes has not gained much attention. In this paper, we present such an approach. We use time series methods to predict the node degree at future time points and combine it with flux balance analysis -- a linear programming method used in biochemistry -- to obtain the structure of future graphs. Furthermore, we explore the predictive graph distribution for different parameter values. We evaluate this method using synthetic and real datasets and demonstrate its utility and applicability.

翻译：动态图嵌入、归纳学习和增量学习促进了节点分类和链接预测等预测任务。然而，从图的时间序列中预测未来时间步的图结构（允许新增节点）尚未引起足够关注。本文提出了一种实现该预测的方法。我们利用时间序列方法预测未来时间点的节点度，并将其与通量平衡分析——一种生物化学中使用的线性规划方法——相结合，以获取未来图的结构。此外，我们探究了不同参数值下预测图分布的规律。通过合成数据集和真实数据集的评估，验证了该方法的实用性和适用性。