How do I create network graph from an adjacency matrix for specific nodes only?

Question

I have an adjacency matrix 5000X5000 and I would like to create a network graph . The requirement is that the user will input the node , and the output would be a graph ( 1st and 2nd degree ) for that particular input node.

I have already tried using Gephi but as the adjacency matrix is huge I am not able to focus on each and every node. So I would like if I could create a graph for specific nodes ( as I am only interested in 1st and 2nd degree connections for each node and not beyond that )

Gephi is UI based so I don't have the code.

Input will be a node_id and output will be a graph corresponding to that node_id ( 1st and 2nd degree connections )

Answer 1

Here is an implementation using networkx:

import networkx as nx
import numpy as np

# make dummy adjacency matrix
a = np.random.rand(100,100)
a = np.tril(a)
a = a>0.95

# make graph from adjaceny matrix
G = nx.from_numpy_matrix(a)


def neigh(G, node, depth):
    """ given starting node, recursively find neighbours
        until desired depth is reached
    """

    node_list = []
    if depth==0:
        node_list.append(node)
    else:
        for neighbor in G.neighbors(node):
            node_list.append(node)
            node_list += neigh(G, neighbor, depth-1)
    return list(set(node_list)) # intermediate conversion to set to lose duplicates. 

# a bit more compressed:
def neigh_short(G, node, depth):
    """ given starting node, recursively find neighbours
        until desired depth is reached
    """

    node_list = [node]
    if depth>0:
        for neighbor in G.neighbors(node)
            node_list += neigh_short(G, neighbor, depth-1)
    return list(set(node_list)) # intermediate conversion to set to lose duplicates. 

# example:
# find all neighbours with distance 2 from node 5:
n = neigh(G, node=5, depth=2)

# extract the respective subgraph from G and store in H
H = G.subgraph(n)