mlsanityData to 14 Oct 2026

Network representation learning: A macro and micro view

arXiv 2111.10772 ↗First posted 21 Nov 2021cs.LG cs.AIcited by 3

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Abstract (arXiv)

Graph is a universe data structure that is widely used to organize data in real-world. Various real-word networks like the transportation network, social and academic network can be represented by graphs. Recent years have witnessed the quick development on representing vertices in the network into a low-dimensional vector space, referred to as network representation learning. Representation learning can facilitate the design of new algorithms on the graph data. In this survey, we conduct a comprehensive review of current literature on network representation learning. Existing algorithms can be categorized into three groups: shallow embedding models, heterogeneous network embedding models, graph neural network based models. We review state-of-the-art algorithms for each category and discuss the essential differences between these algorithms. One advantage of the survey is that we systematically study the underlying theoretical foundations underlying the different categories of algorithms, which offers deep insights for better understanding the development of the network representation learning field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 87 of its 183 references that are in the map. First posted on arXiv 21 Nov 2021.

Builds on

87 of 183 references.

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38 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

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