Statistical inference on random dot product graphs: A survey

被引：0

作者：

Athreya, Avanti ^{[1
]}

Fishkind, Donniell E ^{[1
]}

Tang, Minh ^{[1
]}

Priebe, Carey E ^{[1
]}

Park, Youngser ^{[2
]}

Vogelstein, Joshua T. ^{[3
]}

Levin, Keith ^{[4
]}

Lyzinski, Vince ^{[5
]}

Qin, Yichen ^{[6
]}

Sussman, Daniel L ^{[7
]}

机构：

[1] Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore,MD,2128, United States

[2] Center for Imaging Science, Johns Hopkins University, Baltimore,MD,21218, United States

[3] Department of Biomedical Engineering, Johns Hopkins University, Baltimore,MD,21218, United States

[4] Department of Statistics, University of Michigan, Ann Arbor,MI,48109, United States

[5] Department of Mathematics and Statistics, University of Massachusetts Amherst, MA,01003-9305, United States

[6] Department of Operations, Business Analytics, and Information Systems, College of Business University of Cincinnati, Cincinnati,OH,45221-0211, United States

[7] Department of Mathematics and Statistics, Boston University, Boston,MA,02215, United States

来源：

Journal of Machine Learning Research | 2018年 / 18卷

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摘要：

The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate © 2018 Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Yichen Qin, Youngser Park, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, and Carey E. Priebe .

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