A Survey on Embedding Dynamic Graphs

TL;DR

This survey comprehensively reviews methods for embedding dynamic graphs, addressing challenges like temporal modeling and behavior classification, and discusses applications such as link prediction and anomaly detection.

Contribution

It introduces a formal definition and taxonomy for dynamic graph embedding, covering recent advances and categorizing techniques by algorithmic approach.

Findings

Classifies dynamic behaviors into topological, feature, and process evolution.

Provides a taxonomy of embedding techniques including matrix factorization, deep learning, and temporal models.

Highlights key applications like link prediction, anomaly detection, and diffusion forecasting.

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.