绘制同一社区或分区的网络和分组顶点

Question

我需要在网络中展示（绘制或图示）社区结构。

我现在有这个：

import igraph
from random import randint

def _plot(g, membership=None):
    layout = g.layout("kk")
    visual_style = {}
    visual_style["edge_color"] = "gray"
    visual_style["vertex_size"] = 30
    visual_style["layout"] = layout
    visual_style["bbox"] = (1024, 768)
    visual_style["margin"] = 40
    for vertex in g.vs():
        vertex["label"] = vertex.index
    if membership is not None:
        colors = []
        for i in range(0, max(membership)+1):
            colors.append('%06X' % randint(0, 0xFFFFFF))
        for vertex in g.vs():
            vertex["color"] = str('#') + colors[membership[vertex.index]]
        visual_style["vertex_color"] = g.vs["color"]
    igraph.plot(g, **visual_style)

if __name__ == "__main__":
    karate = igraph.Nexus.get("karate")
    cl = karate.community_fastgreedy()
    membership = cl.as_clustering().membership
    _plot(karate, membership)

但是这些点（节点）分散得很。在其他网络中，这个结果更糟糕。

我希望这些点（节点）能够按颜色分组，聚集在相似的区域。

比如：

Answer 1

要把一个社区的节点聚在一起并突出显示，你应该使用 'mark_groups=True' 这个选项。详细信息可以查看这个链接：http://igraph.org/python/doc/igraph.clustering-pysrc.html#VertexClustering.plot

Answer 2

去掉多个社区之间的连接边，计算没有这些边的布局，然后再把这个布局应用到原始图上。

Answer 3

根据@gabor-csardi的回答，我写了这段代码：

import igraph
from random import randint

def _plot(g, membership=None):
    if membership is not None:
        gcopy = g.copy()
        edges = []
        edges_colors = []
        for edge in g.es():
            if membership[edge.tuple[0]] != membership[edge.tuple[1]]:
                edges.append(edge)
                edges_colors.append("gray")
            else:
                edges_colors.append("black")
        gcopy.delete_edges(edges)
        layout = gcopy.layout("kk")
        g.es["color"] = edges_colors
    else:
        layout = g.layout("kk")
        g.es["color"] = "gray"
    visual_style = {}
    visual_style["vertex_label_dist"] = 0
    visual_style["vertex_shape"] = "circle"
    visual_style["edge_color"] = g.es["color"]
    # visual_style["bbox"] = (4000, 2500)
    visual_style["vertex_size"] = 30
    visual_style["layout"] = layout
    visual_style["bbox"] = (1024, 768)
    visual_style["margin"] = 40
    visual_style["edge_label"] = g.es["weight"]
    for vertex in g.vs():
        vertex["label"] = vertex.index
    if membership is not None:
        colors = []
        for i in range(0, max(membership)+1):
            colors.append('%06X' % randint(0, 0xFFFFFF))
        for vertex in g.vs():
            vertex["color"] = str('#') + colors[membership[vertex.index]]
        visual_style["vertex_color"] = g.vs["color"]
    igraph.plot(g, **visual_style)

if __name__ == "__main__":
    g = igraph.Nexus.get("karate")
    cl = g.community_fastgreedy()
    membership = cl.as_clustering().membership
    _plot(g, membership)

结果：