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Fig. 4 | EURASIP Journal on Bioinformatics and Systems Biology

Fig. 4

From: Graph reconstruction using covariance-based methods

Fig. 4

Workflow for generating synthetic data from a given graph topology. Initially, we construct a graph of interest and then build the adjacency matrix A which elements are ones and zeros. In the next step, we transform A to the positive definite matrix B. We then take an inverse of the positive definite matrix B and calculate the correlation matrix C. In the next step, we factorize the correlation matrix using a Cholesky decomposition and obtain an upper triangular matrix U. We then generate a random matrix R, the columns of which are independent and identically distributed from \(\mathcal {N}(0,1)\). A row size of R is equal to a column size of U, and a column size is equal to a sample size that we want to generate. Finally, we multiply R with U to get a new data with the sample size of interest

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