A Lightweight Interactive Graph Processing Library for Edge Computing in Smart Society
Jun Zhou, Masaaki Kondo · 2021
Edge computing aims to handle the data very close to the source where it is produced, and also provides an effective way to alleviate the burden of cloud computing for the processing of massive volume of data, enabling lower latency, faster response, and more comprehensive data analyses. Meanwhile, graph-based data has emerged especially in the form of social networks, World Wide Web (WWW), transportation, biological networks, and so on, which become high-impact domains of human activity to build smart society. Generally, as the resources on compact end devices are strictly constrained, a graph processing solution with low latency and low overhead is a real necessity for graph analytics in edge computing. In this paper, for the first time to our knowledge, we propose a lightweight interactive graph processing library (GPL) for edge computing in smart society, which is user-friendly and handy to be operated on a single device or among multiple devices. Evaluation is conducted to indicate that the proposed GPL is highly competitive with other established libraries such as igraph, NetworKit or NetworkX based on different graph datasets over a variety of popular algorithms.