https://github.com/kkloste/nexpokit

Matrix Exponential for node centrality, link-prediction, and more
https://github.com/kkloste/nexpokit

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Matrix Exponential for node centrality, link-prediction, and more

• Host: GitHub
• URL: https://github.com/kkloste/nexpokit
• Owner: kkloste
• Created: 2013-07-17T03:23:33.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2014-05-03T17:44:37.000Z (over 10 years ago)
• Last Synced: 2023-08-02T05:12:42.368Z (over 1 year ago)
• Language: Matlab
• Size: 51.5 MB
• Stars: 7
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
---

title: "Network Matrix Exponentials for link-prediction, centrality, and more"

layout: project

---

NEXPOKIT: Network Matrix Exponentials for link-prediction, centrality, and more

===============================================================================

### Kyle Kloster

### David F. Gleich

_These are research codes and may not work for you._

Download

--------

* [nexpokit.tar.gz](nexpokit.tar.gz) (2013-08-05)

Synopsis

--------

    compile % compile the mex files

    G = load_graph('dolphins');

    P = normout(G)';

    x = gexpmq_mex(P,1,11,1e-5,10*size(P,1));

    

    

Reusable codes

--------------

* `gexpm_mex` a C++ mex implementation of Gauss-Southwell with a heap

* `gexpmq_mex` a C++ mex implemetation of pseudo-Gauss-Southwell (rounded Gauss-Seidel) with

  a queue.

* `expmimv_mex` a C++ mex implemetation of an N step Taylor polynomial via Horner rule and "incomplete" matrix-vector products

  a queue.

* `kmatexp` a matlab implementation of an N+1 step Taylor polynomial via Horner rule

* `taydeg.hpp` : contains function for selecting Taylor degree appropriate for input error of “tol”

* `heap.hpp` : contains heap functions used in gexpm_mex

Codes from others

-----------------

* `mexpv` from expokit

* `expv` from expokit

For Higham & Al-Mohy's "expmv":

* `expmv`

* `expmv_tspan`

* `normAm`

* `select_taylor_degree

* `theta_taylor_half.mat`

* `theta_taylor_single.mat`

* `theta_taylor.mat`

Results from the paper

----------------------

To reproduce figures 1 and 2, run:

	

		experiment/localization_demo/example_localization_ljournal.m

To reproduce figure 4, first generate the data by running:

		experiments/accuracy_vs_error/compute_tol_accuracy_data.m

Then, to produce the plots for figure 4, run:

		experiments/accuracy_vs_error/plot_tol_accuracy.m

		

To generate the data for figure 5, run:

		experiments/accuracy_vs_work/compute_steps_accuracy_order.m

To reproduce figure 5, run:

		experiments/accuracy_vs_work/plot_tol_steps_accuracy.m

		

To reproduce figure 6, first generate the data by running:

		experiments/acc_vs_maxnnz/maxnnz_experiment.m

		experiments/acc_vs_maxnnz/maxnnz_experiment_web.m

		experiments/acc_vs_maxnnz/maxnnz_experiment_friend.m				

		experiments/acc_vs_maxnnz/maxnnz_experiment_twitter.m

Then, to produce the plots for figure 6, run

		experiments/acc_vs_maxnnz/maxnnz_plots_finalized.m

To reproduce figure 7, first generate the data by running:

		experiments/runtimes/runtime_experiment.m

		experiments/runtimes/runtime_experiment_web.m		

		experiments/runtimes/runtime_experiment_friend.m		

		experiments/runtimes/runtime_experiment_twitter.m

		experiments/runtimes/runtime_process.m		

Then, to produce the plots for figure 7, run:

		experiments/runtimes/runtime_plot.m

To reproduce figure 8, first generate the data by running:

		experiments/scaling/scaling_study_1.m

		experiments/scaling/scaling_study_s.m

Then, to produce the plots for figure 8, run:

		experiments/scaling/scaling_plots.m