In order to use SVD to analyze network data, you must first choose a suitable network matrix. Depending on the type and source of the data, you may need to preprocess or transform it to obtain a suitable matrix. This could include normalizing, binarizing, weighting, or symmetrizing the data. After this step is complete, you can apply SVD to the network matrix to obtain the singular values and vectors. Software tools such as MATLAB, Python, R, or Excel can be used for this purpose. It is also important to adjust parameters or options such as the number of singular values or vectors, tolerance or convergence criteria, or algorithm or method used in order to optimize performance or accuracy. Lastly, various methods and techniques can be used to analyze the singular values and vectors, such as plotting, ranking, clustering, or labeling. It is also necessary to validate or verify the results of SVD by checking the quality or fit of the decomposition or comparing with other methods or data sources.