Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence

EGI Overview

Abstract

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge computing networks as a fundamental infrastructure for supporting miscellaneous intelligent services like personal voice assistance, video security surveillance, and autonomous driving vehicles. Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated Machine Learning (ML) to the graph domain and promoted Graph Intelligence (GI), which unlocks unprecedented ability in processing, abstracting, and learning from massive data in graph structures. Given the inherent relation between graphs and networks, the interdiscipline of graph representation learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them – GI models principally open a new door for modeling, understanding, and optimizing edge networks, and conversely, edge networks serve as physical support for training, deploying, and accelerating GI models. Driven by this delicate closed-loop, EGI can be widely recognized as a promising solution to fully unleash the potential of edge computing power and is garnering significant attention. Nevertheless, research on EGI yet remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field and specifically, introduces and discusses: 1) fundamentals of edge computing and graph representation learning, 2) emerging techniques centering on the closed loop between graph intelligence and edge networks, i.e., “edge for GI” and “GI for edge”, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI.

Publication
Under review
Liekang Zeng
Liekang Zeng
Ph.D., SMCLab, Sun Yat-sen University

He obtained Ph.D. degree at School of Computer Science and Engineering, Sun Yat-sen University. His research interest lies in building edge intelligence systems with real-time responsiveness, systematic resource efficiency, and theoretical performance guarantee.

Shengyuan Ye
Shengyuan Ye
Ph.D. student at SMCLab

He is a Ph.D. student at School of Computer Science and Engineering, Sun Yat-sen University. His research interests include Resource-efficient AI Systems and Applications with Mobile AI.

Xu Chen
Xu Chen
Professor and Assistant Dean, Sun Yat-sen University
Director, Institute of Advanced Networking & Computing Systems

Xu Chen is a Full Professor with Sun Yat-sen University, Director of Institute of Advanced Networking and Computing Systems (IANCS), and the Vice Director of National Engineering Research Laboratory of Digital Homes. His research interest includes edge computing and cloud computing, federated learning, cloud-native intelligent robots, distributed artificial intelligence, intelligent big data analysis, and computing power network.

Yang Yang
Yang Yang
Professor, IEEE Fellow, HKUST(GZ)
Vice President (Teaching & Learning)

Dr. Yang Yang is currently the Associate Vice-President for Teaching and Learning, the Acting Dean of College of Education Sciences, and a Professor with the IoT Thrust at the Hong Kong University of Science and Technology (Guangzhou). Yang’s research interests include IoT technologies and applications, multi-tier computing networks, 5G/6G mobile communications, intelligent and customized services, and advanced wireless testbeds. He has published more than 300 papers and filed more than 120 technical patents in these research areas.