Maximum A Posteriori Inference of Random Dot Product Graphs via Conic Programming

DOI10.1137/20M1389406MaRDI QIDQ5043284

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Publication date: 21 October 2022

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.02180

zbMATH Keywords

consistencyregularizationmaximum likelihood estimationclusteringconvex relaxationinferenceBayesian inferencegraph embeddingmaximum a posteriorirandom dot product graphconic programminglow ranklatent vectors

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Bayesian inference (62F15) Graph theory (including graph drawing) in computer science (68R10) Positive matrices and their generalizations; cones of matrices (15B48) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Uses Software

• SDPLR
• GenLouvain
• CVXPY
• SCS
• GitHub

Cites Work

• Unnamed Item
• SDPLR
• CVXPY
• SCS
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