The DOI matches its Crossref record; 131 of the 131 references it cites were found in a registry or an index.
Graph Learning: A Survey
Open accessAbstract
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.
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Self-citationA Review of Generalized Zero-Shot Learning Methods
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Metadata
- Authors
- Xia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H.
- Venue
- IEEE Transactions on Artificial Intelligence
- Published
- 2021-04
- Volume
- 2
- Issue
- 2
- Pages
- 109-127
- ISSN
- 2691-4581 (ISSN-L)
- OpenAlex
- W3157999218
- Cited by
- 473
- References
- 131
- FWCI
- 26.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Xia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H. (2021). Graph Learning: A Survey. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/tai.2021.3076021
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