Predicting Graph Structure via Adapted Flux Balance Analysis

TL;DR

This paper introduces an innovative method combining time series prediction with an adapted flux balance analysis to forecast the evolution of graph structures, accommodating vertex changes and applicable to various real-world networks.

Contribution

It presents a novel adaptation of flux balance analysis for dynamic graph prediction, addressing limitations of existing methods that assume fixed vertices.

Findings

Effective on synthetic Preferential Attachment datasets

Successful application to real-world datasets like Facebook and Bitcoin

Outperforms traditional graph prediction methods

Abstract

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.