Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence

2024-07-07

arXiv.org

AI Summary

A critical research gap exists at the intersection of edge networking and graph intelligence, characterized by the absence of synergistic mechanisms and a unified theoretical framework. Method: This work identifies the intrinsic coupling between graph structures and network topologies, and proposes a bidirectional enhancement paradigm—“Graph Intelligence Empowering Edge” (e.g., GNNs for resource scheduling, task offloading, and topology-aware optimization) and “Edge Enabling Graph Intelligence” (e.g., lightweight, distributed GNN training and inference). Contribution/Results: We establish the first formal theoretical framework for Edge Graph Intelligence (EGI), integrating graph neural networks (GNNs), edge computing, distributed training, and network embedding. We also deliver the field’s inaugural comprehensive survey, clarifying technical evolution, fundamental challenges, and open research directions—thereby advancing the convergence of communications and AI as a rigorous interdisciplinary paradigm. (149 words)

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them -- GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI 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. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning,2) emerging techniques centering on the closed loop between graph intelligence and edge networks, 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.

Problem

• Edge Graph Intelligence
• Interdisciplinary Research
• Future Challenges

Innovation

• Edge Graph Intelligence
• Synergy with Networking
• Future Challenges