STVG: an evolutionary graph framework for analyzing fast-evolving networks

Abstract

Sequence of graph snapshots have been commonly utilized in literature to represent changes in a dynamic graph. This approach may be suitable for small-size and slowly evolving graphs; however, it is associated with high storage overhead in massive and fast-evolving graphs because of replication of the entire graph from one snapshot to another at shorter temporal resolutions. This presents a drawback especially where efficient evolutionary analytics relies on the explanatory power of representing the dynamics of the graph across different temporal resolutions. In this paper, we propose a framework based on our Space–Time-varying graph (STVG) formalism which utilizes the Whole-graph approach to model the dynamics of a graph such that the evolution of the graph materializes in the time-varying changes of its Projected graphs. The STVG framework provides an approach to reduce high storage overhead in massively changing graph where new nodes and edges arrive every second. It affords the capability to extract Projected graphs at different time-windows and analyze their metrics across varying temporal resolutions. We demonstrate how the proposed STVG framework can be exploited to identify and extract evolutionary patterns in public bus transit graph using metrics such as graph density, volume and average path length. The results reveal evolutionary patterns in the overall network density, traffic congestion density as well as graph density with respect to bus movement at hourly, daily and monthly temporal resolutions.