Many aspects of graphs have been studied in depth. However, forecasting the structure of a graph at future time steps incorporating unseen, new nodes and edges has not gained much attention. In this paper, we present such an approach. Using a time series of graphs, we forecast graphs at future time steps. We use time series forecasting methods to predict the node degree at future time points and combine these forecasts with flux balance analysis -- a linear programming method used in biochemistry -- to obtain the structure of future graphs. We evaluate this approach using synthetic and real-world datasets and demonstrate its utility and applicability.

翻译：图结构的诸多方面已得到深入研究。然而，结合未见新节点与边来预测未来时间步的图结构尚未获得足够关注。本文提出一种此类方法：利用图的时间序列，我们预测未来时间步的图结构。我们使用时序预测方法预测未来时间点的节点度，并将这些预测结果与通量平衡分析（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。我们通过合成数据集和真实数据集对该方法进行评估，并证明了其实用性与适用性。