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
You’ll have to call nx.draw_networkx_edge_labels(), which will allow you to… draw networkX edge labels 🙂 EDIT: full modified source #!/usr/bin/python import networkx as nx import matplotlib.pyplot as plt G=nx.Graph() i=1 G.add_node(i,pos=(i,i)) G.add_node(2,pos=(2,2)) G.add_node(3,pos=(1,0)) G.add_edge(1,2,weight=0.5) G.add_edge(1,3,weight=9.8) pos=nx.get_node_attributes(G,’pos’) nx.draw(G,pos) labels = nx.get_edge_attributes(G,’weight’) nx.draw_networkx_edge_labels(G,pos,edge_labels=labels) plt.savefig(<wherever>)
The documentation for networkx.draw_networkx_nodes and networkx.draw_networkx_edges explains how to set the node and edge colors. The patches bounding the communities can be made by finding the positions of the nodes for each community and then drawing a patch (e.g. matplotlib.patches.Circle) that contains all positions (and then some). The hard bit is the graph layout / … Read more
Fully fleshed out example with arrows for only the red edges: import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() G.add_edges_from( [(‘A’, ‘B’), (‘A’, ‘C’), (‘D’, ‘B’), (‘E’, ‘C’), (‘E’, ‘F’), (‘B’, ‘H’), (‘B’, ‘G’), (‘B’, ‘F’), (‘C’, ‘G’)]) val_map = {‘A’: 1.0, ‘D’: 0.5714285714285714, ‘H’: 0.0} values = [val_map.get(node, 0.25) for node … Read more
You can use the following approach to set individual node positions and then extract the “pos” dictionary to use when drawing. In [1]: import networkx as nx In [2]: G=nx.Graph() In [3]: G.add_node(1,pos=(1,1)) In [4]: G.add_node(2,pos=(2,2)) In [5]: G.add_edge(1,2) In [6]: pos=nx.get_node_attributes(G,’pos’) In [7]: pos Out[7]: {1: (1, 1), 2: (2, 2)} In [8]: nx.draw(G,pos) … Read more
You could do something like this, to draw nodes from each partition at a particular x coordinate: X, Y = bipartite.sets(B) pos = dict() pos.update( (n, (1, i)) for i, n in enumerate(X) ) # put nodes from X at x=1 pos.update( (n, (2, i)) for i, n in enumerate(Y) ) # put nodes from … Read more
All you need is to specify a color map which maps a color to each node and send it to nx.draw function. To clarify, for a 20 node I want to color the first 10 in blue and the rest in green. The code will be as follows: G = nx.erdos_renyi_graph(20, 0.1) color_map = [] … Read more
import numpy as np import networkx as nx import matplotlib import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation G = nx.Graph() G.add_edges_from([(0,1),(1,2),(2,0)]) fig = plt.figure(figsize=(8,8)) pos=nx.graphviz_layout(G) nc = np.random.random(3) nodes = nx.draw_networkx_nodes(G,pos,node_color=nc) edges = nx.draw_networkx_edges(G,pos) def update(n): nc = np.random.random(3) nodes.set_array(nc) return nodes, anim = FuncAnimation(fig, update, interval=50, blit=True) nx.draw does not return anything, hence … Read more
Another option is to use the constructor csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)]) from scipy.sparse.csr_matrix where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k]. The trick is to generate row_ind and col_ind by iterating over the documents and creating a list of tuples (doc_id, word_id). data would simply be a vector of ones … Read more
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