A sparse matrix is a matrix in which most of the values are zero. The ratio of zero values to non-zero values is known as the sparsity. An estimation of a matrix's sparsity can be helpful when creating hypotheses about the availability of networks. Extensive big sparse matrices are commonly used in machine learning and natural language parsing. It is computationally costly to work with them. Recommendation engines use them for representing products inside a catalog. Computer vision uses sparse matrices and network data structures when working with pictures that contain sections with dark pixels. Network and sparse matrix data structures are also used in social graphs and map layouts. In this chapter, we will cover the following topics:
- Network representations using graphs:
- Social network representation
- Map layouts
- Knowledge graphs...
