I have a list of Connections between two nodes describing similarities of Entries in a Dataset. I'm thinking of vizualising the Entries and their connections to show that there are clusters of very similar entries. Each tuple stands for a pair of very similar nodes. I've chosen weight as 1 for all of them since it's required but I want all edges equally thick. I've started with networkx, problem is I don't really now how to cluster the similar nodes together in a useful manner. I have a List of the connections in a Dataframe: smallSample = [[0, 1492, 1], [12, 937, 1], [16, 989, 1], [18, 371, 1], [18, 1140, 1], [26, 398, 1], [26, 1061, 1], [30, 1823, 1], [33, 1637, 1], [54, 1047, 1], [63, 565, 1]] I Create a graph the following way: import networkx as nx import matplotlib.pyplot as plt G = nx.Graph() for index, row in CC.iterrows(): G.add_edge(CC['source'].loc[index],CC['target'].loc[index], weight =1) pos = nx.spring_layout(G, seed=7) nx.draw_networkx_nodes(G, pos, node_size=5) nx.draw_networkx_edges(G, pos, edgelist=G.edges(), width=0.5) pos = nx.spring_layout(G, k=1, iterations=200) plt.figure(3, figsize=(2000,2000), dpi =2) With the small sample provided above the result looks like this: The result from my real df which consists of thousands of points: How can I Group the linked nodes together so that it is better visible how many of them are in each cluster? I dont want them to overlap so hard, its really not that easy to grasp how many of them are there specially in the big sample. Continue reading...
