In Python, you have the following package which directly treats your problem: "NetworkX"

You essentially construct your network with "Graph()".

Or what do you mean exactly by matrix?

create a python matrix function and deploy it and create it matrix usability by input them

import networkx as nx

import numpy as np

import pandas as pd

G = nx.Graph()

G.add_nodes_from([1, 2, 3, 4, 5])

G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])

A = nx.adjacency_matrix(G)

print(A)

# Calculate the degree of each node.

degrees = [val for (node, val) in G.degree()]

# Calculate the clustering coefficient of each node.

clustering_coefficients = nx.clustering(G)

# Calculate the shortest path between each pair of nodes.

shortest_paths = nx.floyd_warshall(G)

nx.draw(G, with_labels=True)

plt.show()

Building a matrix in social network analysis often involves representing relationships or connections between entities (nodes) in a network. In Python, you can use libraries like NetworkX to work with and create network data structures and matrices.

Learning Resources:

NetworkX Documentation: NetworkX has extensive documentation with examples that can help you learn how to work with social network data.

Online Tutorials: There are many online tutorials and courses that cover social network analysis using Python and NetworkX. Websites like Coursera, edX, and YouTube offer valuable resources.

To learn more and gain a deeper understanding of social network analysis and matrices, consider enrolling in relevant online courses or consulting textbooks and academic resources on the subject. Additionally, you can seek guidance from professors or mentors with expertise in social network analysis.