One way could be to build a graph using NetworkX and obtain the adjacency matrix directly as a dataframe with nx.to_pandas_adjacency. To account for the co-occurrences of the edges in the graph, we can create a nx.MultiGraph, which allows for multiple edges connecting the same pair of nodes: import networkx as nx G = nx.from_edgelist(pair_array, … Read more
One very simple way of doing it is just to pass your multigraph as input to Graph. import networkx as nx G = nx.MultiGraph() G.add_nodes_from([1,2,3]) G.add_edges_from([(1, 2), (1, 2), (1, 3), (2, 3), (2, 3)]) G2 = nx.Graph(G) This will create an undirected graph of your multigraph where multiple edges are merged into single edges. … Read more
I am no expert in this, but here is code that uses the pydot library and its graph_viz dependency. These libraries come with Anaconda Python but are not installed by default, so first do this from the command prompt: conda install pydot Then here is code adapted from Circular Tree. import matplotlib.pyplot as plt import … Read more
This documentation contains a full description A simple example is this: import networkx as nx from networkx.readwrite import json_graph DG = nx.DiGraph() DG.add_edge(‘a’, ‘b’) print json_graph.dumps(DG) You can also take a look at the Javascript/SVG/D3 nice example on adding physics to the graph visualization.
Update for those using networkx 2.x The API has changed from v1.x to v2.x. networkx.degree no longer returns a dict but a DegreeView Object as per the documentation. There is a guide for migrating from 1.x to 2.x here. In this case it basically boils down to using dict(g.degree) instead of d = nx.degree(g). The … Read more
UPDATE (3/2019): as of networkx 2.1, the kwargs are forwarded from draw(), so you should be able to simply call draw() with the edge_color kwarg. Ok, this is kind of hacky, but it works. Here’s what I came up with. The Problem networkx.draw() calls networkx.draw_networkx_nodes(), which then calls pyplot.scatter() to draw the nodes. The problem … Read more
You can use interactive graphs by plotly to plot such a large number of nodes and edges. You can change every attribute like canvas size etc and visualize it more easily by zooming other actions. Example: Import plotly import plotly.graph_objects as go import networkx as nx Add edges as disconnected lines in a single trace … Read more
Python <= 2.7: According to the documentation try: nodesAt5 = filter(lambda (n, d): d[‘at’] == 5, P.nodes(data=True)) or like your approach nodesAt5 = [] for (p, d) in P.nodes(data=True): if d[‘at’] == 5: nodesAt5.append(p) Python 2.7 and 3: nodesAt5 = [x for x,y in P.nodes(data=True) if y[‘at’]==5]
The Graphviz tools appear to display distinct edges. For example, giving this: digraph G { A -> B; A -> B; A -> B; B -> C; B -> A; C -> B; } to dot produces: Graphviz’s input language is pretty simple so you can generate it on your own, though searching for “python … Read more
You may have a networkx MultiGraph instead of a graph and in that case the attribute setting for edges is a little tricker. (You can get a multigraph by loading a graph with more than one edge between nodes). You may be corrupting the data structure by assigning the attribute G.edge[id_source][id_target][‘type’]= value when you need … Read more