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Improved Dynamic Graph Learning through Fault-Tolerant Sparsification

This is the source code for the experiments in the paper 'Improved dynamic graph learning through fault-tolerant sparsification', published in ICML 2019. There are three folders, data, java and matlab, which contain data, Java code and Matlab code respectively.


  • Please unzip data/ in the Matlab working directory. Then one can run matlab/lapsmo.m to get accurary results for Laplacian-regularized estimation, or run matlab/ssl.m to get accurary results for graph SSL.
  • To generate the files prodivided under data folder, one can run the Java code, and at the same time get update time results. Specifically, under the ftss project in /java/ftss folder, can be used to generate signals and obvervations y from an input graph. can be used to get, for every time point, a sparsifier maintained from a fault-tolerant sparsifier and a sparisifier constructed from scratch.

Further Instructions

  • For Laplacian-regularized estimation in lapsmo.m, one may intend to load signals with different noises sigma \in {10^{-3},10^{-2},10^{-1},10^{0}}. To load the signal with the default sigma=10^{-1}, one can use "load y.txt" and "load signal.txt". To load the others, e.g., sigma=10^{-2}, one can use "load y-2.txt" and "load signal-2.txt". Please refer to README under data folder for the naming convention. Lambda can be set in Line 14.
  • For graph SSL in ssl.m, data structures corresponding to different sigma, e.g., "y_ssl" for sigma=10^{-1} and "y_ssl-2" for sigma=10^{-2}, can be constructed by y-ssl.m. Lambda can be set in Line 10.
  • For Java code, the input graph and noise standard deviation sigma can be set in Lines 110 and 112, respectively.
  • For Java code, the parameters can be tuned in Lines 111-119.
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