I have a MultiDiGraph created in networkx for which I am trying to add weights to the edges, after which I assign a new weight based on the frequency/count of the edge occurance. I used the following code to create the graph and add weights, but I'm not sure how to tackle reassigning weights based on count:
g = nx.MultiDiGraph() df = pd.read_csv('G:\cluster_centroids.csv', delimiter=',') df['pos'] = list(zip(df.longitude,df.latitude)) dict_pos = dict(zip(df.cluster_label,df.pos)) #print dict_pos for row in csv.reader(open('G:\edges.csv', 'r')): if '[' in row[1]: # g.add_edges_from(eval(row[1])) for u, v, d in g.edges(data=True): d['weight'] = 1 for u,v,d in g.edges(data=True): print u,v,d Edit
I was able to successfully assign weights to each edge, first part of my original question, with the following:
for u, v, d in g.edges(data=True): d['weight'] = 1 for u,v,d in g.edges(data=True): print u,v,d However, I am still unable to reassign weights based on the number of times an edge occurs (a single edge in my graph can occur multiple times)? I need to accomplish this in order to visualize edges with a higher count differently than edges with a lower count (using edge color or width). I'm not sure how to proceed with reassigning weights based on count, please advise. Below are sample data, and links to my full data set.
Data
Sample Centroids(nodes):
cluster_label,latitude,longitude 0,39.18193382,-77.51885109 1,39.18,-77.27 2,39.17917928,-76.6688633 3,39.1782,-77.2617 4,39.1765,-77.1927 5,39.1762375,-76.8675441 6,39.17468,-76.8204499 7,39.17457332,-77.2807235 8,39.17406072,-77.274685 9,39.1731621,-77.2716502 10,39.17,-77.27 Sample Edges:
user_id,edges 11011,"[[340, 269], [269, 340]]" 80973,"[[398, 279]]" 608473,"[[69, 28]]" 2139671,"[[382, 27], [27, 285]]" 3945641,"[[120, 422], [422, 217], [217, 340], [340, 340]]" 5820642,"[[458, 442]]" 6060732,"[[291, 431]]" 6912362,"[[68, 27]]" 7362602,"[[112, 269]]" Full data:
Centroids(nodes):https://drive.google.com/open?id=0B1lvsCnLWydEdldYc3FQTmdQMmc
Edges: https://drive.google.com/open?id=0B1lvsCnLWydEdEtfM2E3eXViYkk
UPDATE
I was able to solve, at least temporarily, the issue of overly disproportional edge widths due to high edge weight by setting a minLineWidth and multiplying it by the weight:
minLineWidth = 0.25 for u, v, d in g.edges(data=True): d['weight'] = c[u, v]*minLineWidth edges,weights = zip(*nx.get_edge_attributes(g,'weight').items()) and using width=[d['weight'] for u,v, d in g.edges(data=True)] in nx.draw_networkx_edges() as provided in the solution below.
Additionally, I was able to scale color using the following:
# Set Edge Color based on weight values = range(7958) #this is based on the number of edges in the graph, use print len(g.edges()) to determine this jet = cm = plt.get_cmap('YlOrRd') cNorm = colors.Normalize(vmin=0, vmax=values[-1]) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet) colorList = [] for i in range(7958): colorVal = scalarMap.to_rgba(values[i]) colorList.append(colorVal) And then using the argument edge_color=colorList in nx.draw_networkx_edges().
