Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach

DOI10.1007/s10618-017-0492-3zbMath1411.68130OpenAlexW2583675545MaRDI QIDQ1741322

Zheng Li, Philip S. Yu, Ning Yang, Lifang He

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-017-0492-3

zbMATH Keywords

graph embeddingheterogeneous information networkinvertible fusing transformationsparse tensor approximate

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10)

Uses Software

• GraRep
• LINE

Cites Work

• Unnamed Item
• A feasible method for optimization with orthogonality constraints
• Tensor Decompositions and Applications
• Dynamical Approximation by Hierarchical Tucker and Tensor-Train Tensors
• Robust Low-Rank Tensor Recovery: Models and Algorithms
• Tensor decompositions for learning latent variable models
• Dynamical Tensor Approximation
• Fast computation of low-rank matrix approximations
• On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors

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