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Appendix E:
Graph Basics

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
A
(which is called the adjacency matrix of the graph) of size
nxn
, where
n
equals the number of vertices in the graph and element (
i
,
j
) represents quantitive information about the link between vertex
i
and vertex
j
. Most of the graph algorithms can then be described in the language of linear algebra with matrices and vectors.
Basic concepts related to graph algorithms and their representation in the language of linear algebra are described in Graph Fundamentals and Graphs in Linear Algebra. Various storage schemes for sparse matrices are described in Sparse Matrix Storage Formats.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
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Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.