Created
October 31, 2017 23:03
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Community detection using Networkx and Community
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| import numpy as np | |
| import networkx as nx | |
| import matplotlib | |
| # matplotlib.use("TkAgg") | |
| import matplotlib.pyplot as plt | |
| import community | |
| def generate_random_connection_matrix(density=0.1, shape=10, loc=1, scale=2): | |
| res = np.zeros(shape=(shape, shape), dtype=int) | |
| for i in range(res.shape[0]): | |
| for j in range(i+1, res.shape[1]): | |
| if density > np.random.rand(): | |
| weight = abs(int(np.round(np.random.normal(loc, scale)))) | |
| res[i, j] = weight | |
| res[j, i] = weight | |
| return res | |
| def get_node_colors(graph, partition): | |
| if not isinstance(partition, dict): | |
| raise Exception | |
| if not isinstance(graph, nx.Graph): | |
| raise Exception | |
| res = [] | |
| for n in graph.nodes_iter(): | |
| comm_n = partition[n] | |
| res.append(comm_n) | |
| return res | |
| def infer_connections(cm, partition): | |
| if not isinstance(partition, dict): | |
| raise Exception | |
| for com in set(partition.values()): | |
| nodes_in_com = [n for n in partition.keys() if partition[n] == com] | |
| com_cm = cm[nodes_in_com, :][:, nodes_in_com] | |
| com_conns = com_cm[np.triu_indices_from(com_cm, k=1)] | |
| com_median_conn = np.median(com_conns[com_conns > 0]) | |
| for i in nodes_in_com: | |
| for j in nodes_in_com: | |
| if i < j: | |
| if cm[i, j] == 0: | |
| cm[i, j] = com_median_conn | |
| cm[j, i] = com_median_conn | |
| return cm | |
| def plot_graph(graph, partition=None): | |
| if partition is not None: | |
| node_colors = get_node_colors(graph, partition) | |
| else: | |
| node_colors = None | |
| pos = nx.shell_layout(graph) | |
| # nx.draw_shell(graph, with_labels=True) | |
| nx.draw_networkx_nodes(graph, pos, node_color=node_colors) | |
| nx.draw_networkx_edges(graph, pos) | |
| nx.draw_networkx_edge_labels(graph, pos, | |
| edge_labels=nx.get_edge_attributes(graph, "weight"), | |
| font_size=10) | |
| nx.draw_networkx_labels(graph, pos) | |
| def main(): | |
| density = 0.2 | |
| shape = 10 | |
| loc = 1 | |
| scale = 2 | |
| rcm = generate_random_connection_matrix(density, shape, loc, scale) | |
| graph = nx.from_numpy_matrix(rcm) | |
| partition = community.best_partition(graph) | |
| plt.figure() | |
| plt.subplot(2, 1, 1) | |
| plot_graph(graph, partition) | |
| rcm_add = infer_connections(rcm, partition) | |
| graph_add = nx.from_numpy_matrix(rcm_add) | |
| plt.subplot(2, 1, 2) | |
| plot_graph(graph_add, partition) | |
| plt.show() | |
| if __name__ == '__main__': | |
| main() |
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