Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among ...

Keywords: non-negative matrix factorization, graph Laplacian regularization, drug-miRNA associations, weighted k nearest neighbor, sparse similarity matrix Citation: Wang M-N, Li Y, Lei L-L, Ding D-W ...

Analyzing large data sets are challenging. Most data analytics research has proposed parallel algorithms that outside a DBMS because SQL is considered inadequate for complexity computations. R and ...

Resulting Matrix. This produces a new matrix whose order is equal to the number of rows of the first matrix by the number of columns of the second matrix. This is known as the matrix product or the ...

Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In ...

Quantization-based graph transform (QGT) provides a unique perspective on spectrum sensing in cognitive radio networks. Conventional QGT-based spectrum sensing algorithms leverage the topological ...

Quantum graphs—networks composed of vertices connected by edges on which quantum wave dynamics are defined—have emerged as a versatile model for exploring the interplay between geometry ...

The code constructs the adjacency matrix of the molecular graph by converting the generated molecule object to pdb block and reading the CONECT records.. The code extracts seven atomic properties from ...