Question

Given ID2Name and Name2ID as computed above, convert edges into a sparse matrix, G, where there is an entry G[s][t] == 1.0 wherever an edge (s, t) exists.

a) Implement a graph algorithm
b) Use linear algebra techniques
c) Employ machine learning methods
d) Apply statistical models

Answer 1

To create a sparse matrix representing a graph, use a dictionary of dictionaries in Python, setting values to 1.0 where an edge exists between two nodes.

Step-by-step explanation:

A sparse matrix to represent a graph using graph algorithms in the context of data structures and linear algebra. To implement this in Python, one could use a dictionary of dictionaries if the graph edges are given.

Here's a simple function to convert edges into a sparse matrix representation:

def create_sparse_matrix(edges):
G = {}
for s, t in edges:
if s not in G:
G[s] = {}
G[s][t] = 1.0
return G

This function assumes that 'edges' is a list of tuples where each tuple represents an edge in the graph. The sparse matrix 'G' is then a dictionary where 'G[s][t]' is set to 1.0 if there is an edge from 's' to 't'.