用改进的代谢通量分析预测动态图结构变化
Predicting Graph Structure via Adapted Flux Balance Analysis
- 将生化中的通量平衡分析改造为动态图预测工具
- 在真实和合成数据上准确预测未来图结构，支持节点增减
- 适合研究网络演化、异常检测的学者与工程师
许多动态过程（如通信网、交通网）可由离散时间序列的图来描述。建模此类时间序列的动态性，可预测未来时刻的图结构，用于异常检测等应用。现有方法常假设节点在连续图间不变，存在局限。本文提出结合时序预测方法与改进的通量平衡分析（FBA），FBA被适配以引入适用于增长型图的各种约束。在合成数据（基于优先连接模型构建）和真实数据集（UCI Message、HePH、Facebook、Bitcoin）上的实证评估表明该方法有效。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。