An Interactive and Reductive Graph Processing Library for Edge Computing in Smart Society

• Published in: IEICE Transactions on Information and Systems Vol. E106.D; no. 3; pp. 319 - 327
• Main Authors: KONDO, Masaaki, ZHOU, Jun
• Format: Journal Article
• Language: English
• Published: Tokyo The Institute of Electronics, Information and Communication Engineers 01.03.2023; Japan Science and Technology Agency
• Subjects: Algorithms; boost graph library; Cloud computing; Data structures; Domains; Edge computing; graph processing; graph reduction; Graphical representations; interactivity; Internet of Things; Libraries; Network latency; network socket; Provisioning; Recommender systems; Resource allocation; Social networks; Society; Transport buildings, stations and terminals
• ISSN: 0916-8532; 1745-1361; 1745-1361
• DOI: 10.1587/transinf.2022FCP0008

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.