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python - 如何清晰地绘制大型网络

问题描述

我有一个 cora 引用网络数据，它有 2708 个节点和 5429 个边。我试图用networkx来绘制它

import scipy.sparse as sp
import numpy as np
import matplotlib.pyplot as plt

import networkx as nx

def encode_onehot(labels):
    classes = set(labels)
    classes_dict = {c: np.identity(len(classes))[i, :] for i, c in enumerate(classes)}
    labels_onehot = np.array(list(map(classes_dict.get, labels)), dtype=np.int32)
    return labels_onehot

def load_data(path="data/cora/", dataset="cora"):
    print('Loading {} dataset...'.format(dataset))

    idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset), dtype=np.dtype(str))
    features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
    labels = encode_onehot(idx_features_labels[:, -1])

    # build graph
    idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
    idx_map = {j: i for i, j in enumerate(idx)}
    edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.int32)
    edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
                     dtype=np.int32).reshape(edges_unordered.shape)
    adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
                        shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)

    # build symmetric adjacency matrix
    adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)

    print('Dataset has {} nodes, {} edges, {} features.'.format(adj.shape[0], edges.shape[0], features.shape[1]))

    return features.todense(), adj, labels


X, A, y = load_data(dataset='cora')

G = nx.from_scipy_sparse_matrix(A)

nx.draw_networkx(G)
plt.show()

coranetworkplot

如您所见，这很难看到。有什么方法可以让它更容易看到吗？任何建议都会被采纳。

标签： pythonmatplotlibnetworkx

解决方案

您可以使用 pyvis.network 进行交互式可视化。您还可以将图形导出到外部文件或在笔记本中内联绘制网络。

我通常使用此功能为自己绘制图表。

def draw_graph3(networkx_graph,notebook=True,output_filename='graph.html',show_buttons=False,only_physics_buttons=False):
        """
        This function accepts a networkx graph object,
        converts it to a pyvis network object preserving its node and edge attributes,
        and both returns and saves a dynamic network visualization.

        Valid node attributes include:
            "size", "value", "title", "x", "y", "label", "color".

            (For more info: https://pyvis.readthedocs.io/en/latest/documentation.html#pyvis.network.Network.add_node)

        Valid edge attributes include:
            "arrowStrikethrough", "hidden", "physics", "title", "value", "width"

            (For more info: https://pyvis.readthedocs.io/en/latest/documentation.html#pyvis.network.Network.add_edge)


        Args:
            networkx_graph: The graph to convert and display
            notebook: Display in Jupyter?
            output_filename: Where to save the converted network
            show_buttons: Show buttons in saved version of network?
            only_physics_buttons: Show only buttons controlling physics of network?
        """

        # import
        from pyvis import network as net

        # make a pyvis network
        pyvis_graph = net.Network(notebook=notebook)
        pyvis_graph.width = '1000px'
        # for each node and its attributes in the networkx graph
        for node,node_attrs in networkx_graph.nodes(data=True):
            pyvis_graph.add_node(node,**node_attrs)
    #         print(node,node_attrs)

        # for each edge and its attributes in the networkx graph
        for source,target,edge_attrs in networkx_graph.edges(data=True):
            # if value/width not specified directly, and weight is specified, set 'value' to 'weight'
            if not 'value' in edge_attrs and not 'width' in edge_attrs and 'weight' in edge_attrs:
                # place at key 'value' the weight of the edge
                edge_attrs['value']=edge_attrs['weight']
            # add the edge
            pyvis_graph.add_edge(source,target,**edge_attrs)

        # turn buttons on
        if show_buttons:
            if only_physics_buttons:
                pyvis_graph.show_buttons(filter_=['physics'])
            else:
                pyvis_graph.show_buttons()

        # return and also save
        return pyvis_graph.show(output_filename)

您可以通过调用此函数来可视化图形

draw_graph3(your_graph,output_filename='graph_output.html', notebook=False)

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