igraph Graph from numpy or pandas adjacency matrix

In igraph you can use igraph.Graph.Adjacency to create a graph from an adjacency matrix without having to use zip. There are some things to be aware of when a weighted adjacency matrix is used and stored in a np.array or pd.DataFrame.

• igraph.Graph.Adjacency can't take an np.array as argument, but that is easily solved using tolist.

• Integers in adjacency-matrix are interpreted as number of edges between nodes rather than weights, solved by using adjacency as boolean.

An example of how to do it:

import igraph
import pandas as pd

node_names = ['A', 'B', 'C']
a = pd.DataFrame([[1,2,3],[3,1,1],[4,0,2]], index=node_names, columns=node_names)

# Get the values as np.array, it's more convenenient.
A = a.values

# Create graph, A.astype(bool).tolist() or (A / A).tolist() can also be used.
g = igraph.Graph.Adjacency((A > 0).tolist())

# Add edge weights and node labels.
g.es['weight'] = A[A.nonzero()]
g.vs['label'] = node_names  # or a.index/a.columns

You can reconstruct your adjacency dataframe using get_adjacency by:

df_from_g = pd.DataFrame(g.get_adjacency(attribute='weight').data,
                         columns=g.vs['label'], index=g.vs['label'])
(df_from_g == a).all().all()  # --> True

Strictly speaking, an adjacency matrix is boolean, with 1 indicating the presence of a connection and 0 indicating the absence. Since many of the values in your a_numpy matrix are > 1, I will assume that they correspond to edge weights in your graph.

import igraph

# get the row, col indices of the non-zero elements in your adjacency matrix
conn_indices = np.where(a_numpy)

# get the weights corresponding to these indices
weights = a_numpy[conn_indices]

# a sequence of (i, j) tuples, each corresponding to an edge from i -> j
edges = zip(*conn_indices)

# initialize the graph from the edge sequence
G = igraph.Graph(edges=edges, directed=True)

# assign node names and weights to be attributes of the vertices and edges
# respectively
G.vs['label'] = node_names
G.es['weight'] = weights

# I will also assign the weights to the 'width' attribute of the edges. this
# means that igraph.plot will set the line thicknesses according to the edge
# weights
G.es['width'] = weights

# plot the graph, just for fun
igraph.plot(G, layout="rt", labels=True, margin=80)