Matrix Resolvent Eigenembeddings for Dynamic Graphs
Author:
Affiliation:
1. Thomas J. Watson Research Center,IBM Research,Yorktown Heights,NY,USA
2. Michigan State University,Dept. of Electrical and Computer Engineering,East Lansing,MI,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096476.pdf?arnumber=10096476
Reference24 articles.
1. Incremental spectral clustering by efficiently updating the eigen-system
2. A semidiscrete matrix decomposition for latent semantic indexing information retrieval
3. Fast approximate spectral clustering for dynamic networks;martin;Proceedings of the 35th International Conference on Machine Learning,2018
4. The university of Florida sparse matrix collection
5. Projection techniques to update the truncated SVD of evolving matrices with applications;kalantzis;International Conference on Machine Learning,2021
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1. Gradient-Based Spectral Embeddings of Random Dot Product Graphs;IEEE Transactions on Signal and Information Processing over Networks;2024
2. Rayleigh-Ritz Based Updates of the Multilinear Singular Value Decomposition;2023 57th Asilomar Conference on Signals, Systems, and Computers;2023-10-29
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