Many dynamic processes such as telecommunication and transport networks can be described through discrete time series of graphs. Modelling the dynamics of such time series enables prediction of graph structure at future time steps, which can be used in applications such as detection of anomalies. Existing approaches for graph prediction have limitations such as assuming that the vertices do not to change between consecutive graphs. To address this, we propose to exploit time series prediction methods in combination with an adapted form of flux balance analysis (FBA), a linear programming method originating from biochemistry. FBA is adapted to incorporate various constraints applicable to the scenario of growing graphs. Empirical evaluations on synthetic datasets (constructed via Preferential Attachment model) and real datasets (UCI Message, HePH, Facebook, Bitcoin) demonstrate the efficacy of the proposed approach.

翻译：许多动态过程（如通信网络与交通网络）可通过离散时间序列图进行描述。对此类时间序列的动态建模能够预测未来时间步的图结构，可用于异常检测等应用场景。现有图预测方法存在局限性，例如假设连续图之间的顶点保持不变。为解决此问题，我们提出结合时间序列预测方法与改进型通量平衡分析（FBA）——一种源自生物化学的线性规划方法。改进后的FBA能够纳入适用于增长图场景的多种约束条件。在合成数据集（通过偏好依附模型构建）和真实数据集（UCI Message、HePH、Facebook、Bitcoin）上的实证评估验证了所提方法的有效性。