In this demo, we are going to show the basic operations of the GSPBox. To lauch the toolbox, just go into the repository where the GSPBox was extracted and type:
A banner will popup telling you that everything happens correctly. To speedup some processing, you might want to compile some mexfile. Refer tofor more informations. However, if the compilation is not working on your computer, keep quiet, everything should still work and most of the routine are implemented only in matlab.
Most likely, the first thing you would like to do is to create a graph. To do so, you only need the adjacendy or the weight matrix \(W\). Once you have it, you can construct a graph using:
G = gsp_graph(W);
This function will create a full structure ready to be used with the toolbox. To know a bit more about what is in this structure, you can refer to the help of the function.
The GSPBox contains also a list of graph generators. To see a full list of these graphs, type:
This code produces the following output:
GSPBOX - Graphs Specific graphs gsp_swiss_roll - Create swiss roll graph gsp_david_sensor_network - Create the sensor newtwork from david gsp_ring - Create the ring graph gsp_path - Create the path graph gsp_airfoil - Create the airfoil graph gsp_comet - Create the comet graph gsp_erdos_renyi - Create a erdos renyi graph gsp_minnesota - Create Minnesota road graph gsp_low_stretch_tree - Create a low stretch tree graph gsp_sensor - Create a random sensor graph gsp_random_regular - Create a random regular graph gsp_random_ring - Create a random ring graph gsp_full_connected - Create a fully connected graph gsp_nn_graph - Create a nearest neighbors graph gsp_rmse_mv_graph - Create a nearest neighbors graph with missing values gsp_sphere - Create a spherical-shaped graph gsp_cube - Create a cubical-shaped graph gsp_2dgrid - Create a 2d-grid graph gsp_torus - Create a torus graph gsp_logo - Create a GSP logo graph gsp_community - Create a community graph gsp_bunny - Create a bunny graph gsp_spiral - Create a spiral graph gsp_stochastic_block_graph - Create a graph with the stochastic block model Hypergraphs gsp_nn_hypergraph - Create an hyper nearest neighbor graph gsp_hypergraph - Create an hypergraph Utils gsp_graph_default_parameters- Initialise all parameters for a graph gsp_graph_default_plotting_parameters- Initialise all plotting parameters for a graph gsp_graph - Create a graph from a weight matrix gsp_update_weights - Update the weights of a graph gsp_update_coordinates - Update the coordinate of a graph gsp_components - Cuts non connected graph into several connected ones gsp_subgraph - Create a subgraph gsp_graph_product - Compute graph product between two graphs gsp_line_graph - Create the Line Graph (or edge-to-vertex dual graph) of a graph gsp_jtv_graph - Add time information to the graph structure For help, bug reports, suggestions etc. please send email to gspbox 'dash' support 'at' groupes 'dot' epfl 'dot' ch
For this demo, we will use the graph. You can load it using:
G = gsp_logo
This code produces the following output:
G = struct with fields: W: [1130×1130 double] coords: [1130×2 double] info: [1×1 struct] plotting: [1×1 struct] limits: [0 640 -400 0] A: [1130×1130 logical] N: 1130 type: 'unknown' directed: 0 hypergraph: 0 lap_type: 'combinatorial' L: [1130×1130 double] d: [1130×1 double] Ne: 3131
Here observe the attribute of the structure G.
In the folder 'plotting', the GSPBox contains some plotting routine. For instance, we can plot a graph using:
Wonderful! Isn't it? Now, let us start to analyse this graph. To compute graph Fourier transform or exact graph filtering, you need to precompute the Fourier basis of the graph. This operation could be relatively long since it involves a full diagonalization of the Laplacian. Don't worry, you do not need to perform this operation to filter signals on graph. The fourier basis is computed by:
G = gsp_compute_fourier_basis(G);
The functionadd two new fields to the structure G:
The fourier eigenvectors does look like a sinusoide on the graph. Let's plot the second and the third ones. (The first one is constant!):
gsp_plot_signal(G,G.U(:,2)); title('Second eigenvector') subplot(212) gsp_plot_signal(G,G.U(:,3)); title('Third eigenvector')
Now, we are going to show a basic filtering operation. Filters are usually defined in the spectral domain. To define the following filter
just write in Matlab:
tau = 1; h = @(x) 1./(1+tau*x);
Hint: You can define filterbank using cell array!
Let's display this filter:
To apply the filter to a given signal, you only need to run a single function:
% Create a signal f = zeros(G.N,1); f(G.info.idx_g) = -1; f(G.info.idx_s) = 1; f(G.info.idx_p) = -0.5; f = f + 0.3*randn(G.N,1); % Remove the noise f2 = gsp_filter(G,h,f);
gsp_filter is actually a shortcut to. gsp_filter_analysis performs the analysis operator associated to a filterbank. See the for more information.
Finnaly, we display the result of this low pass filtering on the graph:
figure; subplot(211) gsp_plot_signal(G,f); title('Signal with noise') subplot(212) gsp_plot_signal(G,f2); title('Signal denoised');
Enjoy the GSPBOX !