Create Networks from Adjacency and Incidence Matrices
This section uses Pandas, NumPy.
Mathematical graphs as collections of nodes and edges are not the only way to represent complex networks. Researchers and practitioners often use tabular (matrix) data to describe networks. The two most popular matrix-based descriptions are adjacency and incidence matrices. (You may want to remind yourself of the definitions of adjacency and incidence on the bulleted listhere.)
Adjacency Matrix, the Python Way
An adjacency matrix A is a square N×N matrix, where N is the size of the graph to be defined. The row and column indexes indicate the source and target nodes, respectively. Depending on the network type, the acceptable range, properties, and interpretation ...