Aggarwal, M and Murty, MN (2020) Node Representations. [Book Chapter]
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Official URL: https://doi.org/10.1007/978-981-33-4022-0_4
Abstract
Downstream ML tasks can exploit the low-dimensional node embeddings by using them as the inputs to traditional machine learning models. These node-level downstream tasks include node classification, node clustering, recommendation, link prediction, and visualization. In this chapter, we discuss node embedding techniques. These techniques are based on one of random walk, matrix factorization, or deep learning. Further, some algorithms learn representations in an unsupervised setting while others learn in a supervised setting. We finally present comparison of these algorithms according to their performance on downstream tasks.
| Item Type: | Book Chapter |
|---|---|
| Publication: | SpringerBriefs in Applied Sciences and Technology |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to the Springer Science and Business Media Deutschland GmbH. |
| Keywords: | Embeddings; Factorization, Embedding technique; Link prediction; Low dimensional; Machine learning models; Matrix factorizations; Node clustering; Random Walk, Deep learning |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 09 Nov 2023 05:26 |
| Last Modified: | 09 Nov 2023 05:26 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81414 |
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