A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings
A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings
复制标题
与谱特征向量嵌入密切相关的灵活的基于PageRank的图嵌入框架
DOI:
10.1007/s41468-023-00129-6
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Gleich, David F.
中科院分区:
文献类型:
--
作者:
Shur, Disha;Huang, Yufan;Gleich, David F.
We study a simple embedding technique based on a matrix of personalized PageRank vectors seeded on a random set of nodes. We show that the embedding produced by the leading singular vectors of an element-wise logarithm of this matrix is related to the spectral embedding of Laplacian eigenvectors for degree regular graphs. Moreover, this log-PageRank embedding procedure produces useful results for global graph visualization even when the spectral embedding does not. Most importantly, the general nature of this embedding strategy opens up many emerging applications, where eigenvector and spectral techniques may not be well established, to the PageRank-based relatives. For instance, similar techniques can be used on PageRank vectors from hypergraphs to get “spectral-like” embeddings.
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DOI:
10.1073/pnas.1800683115
发表时间:
2018-11-27
影响因子:
11.1
作者:
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通讯作者:
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影响因子:
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DOI:
10.1145/3442381.3450035
发表时间:
2020-06
期刊:
Proceedings of the Web Conference 2021
影响因子:
--
作者:
Francesco Tudisco;Austin R. Benson;Konstantin Prokopchik
通讯作者:
Francesco Tudisco;Austin R. Benson;Konstantin Prokopchik
DOI:
10.21203/rs.3.rs-148524/v1
发表时间:
2021
期刊:
ArXiv
影响因子:
--
作者:
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通讯作者:
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