- Title
- Graph learning : a survey
- Creator
- Xia, Feng; Sun, Ke; Yu, Shuo; Aziz, Abdul; Wan, Liangtian; Pan, Shirui; Liu, Huan
- Date
- 2021
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190036
- Identifier
- vital:17546
- Identifier
-
https://doi.org/10.1109/TAI.2021.3076021
- Identifier
- ISSN:2691-4581 (ISSN)
- 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. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in different areas such as social computing, information retrieval, computer vision, bioinformatics, economics, and e-commence. Second, it presents insights into open areas of research in the field. Third, it aims to stimulate new research ideas and more interests in graph learning. © IEEE Transactions on Artificial Intelligence 2020.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Artificial Intelligence Vol. 2, no. 2 (2021), p. 109-127
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © IEEE Transactions on Artificial Intelligence 2020
- Rights
- Open Access
- Subject
- 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; 4611 Machine learning; Deep learning; graph data; graph learning; graph neural networks (GNNs); machine learning; network embedding; network representation learning (NRL)
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