survey

A Survey on Embedding Dynamic Graphs

Authors:

Claudio D. T. Barros,

Matheus R. F. Mendonça,

Alex B. Vieira,

Artur ZivianiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 1

Article No.: 10, Pages 1 - 37

https://doi.org/10.1145/3483595

Published: 23 November 2021 Publication History

Abstract

Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.

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Cited By

Ye ZZhou LLi MZhang WLiu ZZhao H(2024)Multichannel Adaptive Data Mixture Augmentation for Graph Neural NetworksInternational Journal of Data Warehousing and Mining10.4018/IJDWM.34997520:1(1-14)Online publication date: 5-Aug-2024
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A Survey on Embedding Dynamic Graphs

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 1

January 2023

860 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3492451

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 23 November 2021

Accepted: 01 August 2021

Revised: 01 July 2021

Received: 01 November 2020

Published in CSUR Volume 55, Issue 1

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https://doi.org/10.3390/fi16080295
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Zhang XJiao PGao MLi TWu YWu HZhao Z(2024)VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337754819(4272-4284)Online publication date: 18-Mar-2024
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