Many applications in data science and machine learning involve working with data represented as graphs. Graph themselves can be represented in a variety of ways which describe relations between graph vertices and edges. Often, a graph is represented as a sparse matrix
(which is called the adjacency matrix of the graph) of size
equals the number of vertices in the graph and element (
) represents quantitive information about the link between vertex
. Most of the graph algorithms can then be described in the language of linear algebra with matrices and vectors.