LocalTGEP: A Lightweight Edge Partitioner for Time-Varying Graph

Abstract

Graph edge partitioning (GEP), the allocation of edges into different parts through cut vertices, is essential for the analytics of large-scale graphs. Most GEP models cannot be directly applied to a time-varying graph unless repartitioning the entire graph, which leads to a large consumption of resources. Although a few studies have focused on time-varying graph edge partitioning, they have ignored the memory consumption during the partitioning process. Therefore, a lightweight edge partitioner, referred to as LocalTGEP, broadening the application to time-varying graphs, is proposed herein. Three superiorities of LocalTGEP are highlighted as follows: 1) A satisfactory partitioning quality for a time-varying graph can be achieved without requiring global information owing to the local edge partitioning. 2) Memory consumption of the partitioner is significantly reduced using a novel storage framework of graph data in LocalTGEP. 3) The quality and efficiency of time-varying graph edge partitioning are optimized by designing the push and pop stages in LocalTGEP. Extensive experimental results obtained on 12 real-world graphs demonstrate that LocalTGEP outperforms rival algorithms in terms of memory consumption, partitioning quality, and efficiency.