Ggtcf

I'm trying to get through a toy example of building an adjacency matrix from a list, but already I can't quite figure it out. I am thinking in terms of .loc() but I'm not sure how to index correctly.

'nodes':['A', 'B', 'C', 'D', 'E'],
 'edges':[('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'), 
 ('D', 'E'), ('E', 'A'),('E', 'B'), ('E', 'C')]

I've started to build the matrix with:

n = len(graph['nodes'])
adj_matr = pd.DataFrame(0, columns = graph['nodes'], index = graph['edges'])

but now I'm not sure how to fill it in. I think there's an easy one liner, maybe with a list comprehension?

Expected output:

 A B C D E
A 0 1 0 1 0
B 0 0 1 0 1
C 0 0 0 1 0
D 0 0 0 0 1
E 1 1 1 0 0

A simple way to obtain the adjacency matrix is by using NetworkX

d = 'nodes':['A', 'B', 'C', 'D', 'E'],
 'edges':[('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'), 
 ('D', 'E'), ('E', 'A'),('E', 'B'), ('E', 'C')]

It appears that from your adjacency matrix the graph is directed. You can create a directed graph as shown bellow and define its nodes and edges from the dictionary with:

import networkx as nx
g = nx.DiGraph()
g.add_nodes_from(d['nodes'])
g.add_edges_from(d['edges'])

And then you can obtain the adjacency matrix from the network with nx.adjacency_matrix:

m = nx.adjacency_matrix(g)
m.todense()

matrix([[0, 1, 0, 1, 0],
 [0, 0, 1, 0, 1],
 [0, 0, 0, 1, 0],
 [0, 0, 0, 0, 1],
 [1, 1, 1, 0, 0]], dtype=int64)

And for a dataframe with the corresponding nodes as columns you can do:

pd.DataFrame(m.todense(), columns=nx.nodes(g))

 A B C D E
0 0 1 0 1 0
1 0 0 1 0 1
2 0 0 0 1 0
3 0 0 0 0 1
4 1 1 1 0 0

for undirected graph

graph = 'nodes': ['A', 'B', 'C', 'D', 'E'],
 'edges': [('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'),
 ('D', 'E'), ('E', 'A'), ('E', 'B'), ('E', 'C')]
n = len(graph['nodes'])
adj_matr = pd.DataFrame(0, columns=graph['nodes'], index=graph['nodes'])
for i in graph['edges']:
 adj_matr.at[i[0], i[1]] = 1
 adj_matr.at[i[1], i[0]] = 1


print(adj_matr)

 A B C D E
A 0 1 0 1 1
B 1 0 1 0 1
C 0 1 0 1 1
D 1 0 1 0 1
E 1 1 1 1 0

for directed graph:

graph = 'nodes': ['A', 'B', 'C', 'D', 'E'],
 'edges': [('A', 'B'), ('A', 'D'), ('B', 'C'), ('B', 'E'), ('C', 'D'),
 ('D', 'E'), ('E', 'A'), ('E', 'B'), ('E', 'C')]
n = len(graph['nodes'])
adj_matr = pd.DataFrame(0, columns=graph['nodes'], index=graph['nodes'])
print(adj_matr)
for i in graph['edges']:
 adj_matr.at[i[0], i[1]] = 1
 # adj_matr.at[i[1], i[0]] = 1

print(adj_matr)

 A B C D E
A 0 1 0 1 0
B 0 0 1 0 1
C 0 0 0 1 0
D 0 0 0 0 1
E 1 1 1 0 0

For a directed graph you can use:

df = pd.DataFrame(graph['edges'], columns=['From', 'To'])
df['Edge'] = 1
adj = df.pivot(index='From', columns='To', values='Edge').fillna(0)