Graph Learning: A Survey

TL;DR

This survey provides a comprehensive overview of graph learning methods, applications, and future directions, highlighting the integration of machine learning techniques with graph-structured data across various domains.

Contribution

It systematically reviews state-of-the-art graph learning techniques, categorizing them into four main types and discussing their applications and future research opportunities.

Findings

Identifies four main categories of graph learning methods.

Highlights diverse applications in text, images, and knowledge graphs.

Discusses promising future research directions.

Abstract

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.