ネットワークグラフで関係性を力学レイアウトで描く

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

rng = np.random.default_rng(42)

G = nx.Graph()

# 3つのコミュニティを作成
communities = {
    "開発": ["田中", "鈴木", "佐藤", "高橋", "伊藤"],
    "営業": ["渡辺", "山本", "中村", "小林", "加藤"],
    "企画": ["吉田", "山田", "松本", "井上", "木村"],
}
colors_map = {"開発": "#2563eb", "営業": "#10b981", "企画": "#ef4444"}

node_colors = []
for dept, members in communities.items():
    G.add_nodes_from(members)
    node_colors.extend([colors_map[dept]] * len(members))
    # コミュニティ内は密に接続
    for i, a in enumerate(members):
        for b in members[i + 1 :]:
            if rng.random() < 0.7:
                G.add_edge(a, b, weight=rng.uniform(1, 3))

# コミュニティ間は疎に接続
cross_edges = [
    ("田中", "渡辺"), ("鈴木", "吉田"), ("高橋", "山本"),
    ("伊藤", "松本"), ("中村", "山田"),
]
for a, b in cross_edges:
    G.add_edge(a, b, weight=rng.uniform(0.3, 1.0))

pos = nx.spring_layout(G, seed=42, k=1.5)
degrees = dict(G.degree())
node_sizes = [300 + degrees[n] * 120 for n in G.nodes()]
edge_weights = [G[u][v]["weight"] for u, v in G.edges()]

fig, ax = plt.subplots(figsize=(9, 7))
nx.draw_networkx_edges(G, pos, ax=ax, alpha=0.3, width=edge_weights)
nx.draw_networkx_nodes(
    G, pos, ax=ax, node_color=node_colors,
    node_size=node_sizes, edgecolors="white", linewidths=1.5,
)
nx.draw_networkx_labels(G, pos, ax=ax, font_size=9, font_family="sans-serif")

# 凡例
for dept, color in colors_map.items():
    ax.scatter([], [], c=color, s=100, label=dept)
ax.legend(loc="upper left", title="部署")
ax.set_title("部署間コミュニケーション ネットワーク")
ax.axis("off")

fig.tight_layout()
plt.show()