其他
karateclub库 | 计算社交网络中节点的向量
近期活动
2022年5月16号 Python数据挖掘2022五月直播开始报名啦
karateclub是小规模图挖掘研究的一把瑞士军刀， 可以对图形结构化数据进行无监督学习。
首先，可以计算出节点、图的特征向量 其次，它包括多种重叠和非重叠的社区发现方法。
数据格式
karateclub假设用户提供的用于节点嵌入和社区检测的 NetworkX 图具有以下重要属性：
节点用整数索引 节点索引从零开始，索引是连续的
节点的属性矩阵可以提供为 scipy sparse 和 numpy 数组。返回的社区成员字典和嵌入矩阵使用相同的数字连续索引。
安装
pip3 install karateclub
准备数据
import pandas as pd
df = pd.read_csv('karate_club_graph.csv')
print(df.columns)
print()
print(df.head().to_markdown())
print()
edges = list(zip(df['src'], df['tgt']))
print(edges)
Run
Index(['src', 'tgt'], dtype='object')
| | src | tgt |
|---:|------:|------:|
| 0 | 0 | 1 |
| 1 | 0 | 2 |
| 2 | 0 | 3 |
| 3 | 0 | 4 |
| 4 | 0 | 5 |
[(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32), (3, 7), (3, 12), (3, 13), (4, 6), (4, 10), (5, 6), (5, 10), (5, 16), (6, 16), (8, 30), (8, 32), (8, 33), (9, 33), (13, 33), (14, 32), (14, 33), (15, 32), (15, 33), (18, 32), (18, 33), (19, 33), (20, 32), (20, 33), (22, 32), (22, 33), (23, 25), (23, 27), (23, 29), (23, 32), (23, 33), (24, 25), (24, 27), (24, 31), (25, 31), (26, 29), (26, 33), (27, 33), (28, 31), (28, 33), (29, 32), (29, 33), (30, 32), (30, 33), (31, 32), (31, 33), (32, 33)]
import networkx as nx
graph = nx.Graph()
graph.add_edges_from(edges)
nx.draw(graph)
Run
社区发现
现在让我们使用LabelPropagation算法来发现网络中的社区结构。
from karateclub import LabelPropagation
model = LabelPropagation()
model.fit(graph)
cluster_membership = model.get_memberships()
cluster_membership
Run
{23: 8,
33: 8,
5: 10,
7: 1,
28: 31,
4: 10,
3: 1,
31: 31,
20: 8,
19: 1,
6: 10,
32: 8,
29: 8,
9: 1,
14: 8,
2: 1,
0: 1,
17: 1,
25: 31,
22: 8,
11: 1,
13: 1,
1: 1,
24: 31,
15: 8,
18: 8,
26: 8,
27: 8,
16: 10,
12: 1,
30: 8,
21: 1,
8: 8,
10: 10}
在有34个节点的图中，发现了4个社区，分别是1、8、10、31。
Node embeddings
计算节点的向量。使用 Diff2vec 拟合数据的节点嵌入(向量)，具有少量维度、每个源节点的扩散和短欧拉游走。
from karateclub import Diff2Vec
model = Diff2Vec(diffusion_number=2,
diffusion_cover=20,
dimensions=5)
model.fit(graph)
X = model.get_embedding()
X.shape
Run
(34, 5)
X
Run
array([[ 1.3687179 , -0.33502993, -0.3294797 , 0.40154558, 1.0270709 ],
[ 0.88167036, -0.3201618 , -0.34293872, 0.41519755, 0.71964073],
[ 0.8756805 , -0.21934716, -0.33261183, 0.33785722, 0.51631075],
[ 0.9768452 , -0.39260587, -0.39460638, 0.28851682, 0.8665034 ],
[ 0.4809215 , -0.28729865, -0.19276802, 0.22588767, 0.07305563],
[ 0.5580538 , -0.28137547, -0.1947159 , 0.23712516, 0.49257705],
[ 0.23477663, 0.04262228, 0.07154325, 0.02909669, 0.33999097],
[ 1.1882199 , -0.21742308, -0.26985615, 0.44171503, 0.6679048 ],
[ 1.0287609 , -0.27409104, -0.04119629, 0.30143994, 0.704676 ],
[ 0.5700088 , -0.26341844, 0.01560158, -0.08039217, 0.41796318],
[ 0.5753763 , -0.2242508 , -0.1795436 , 0.0705331 , 0.46571913],
[ 0.46763912, -0.17108741, -0.22459361, 0.03058788, 0.05998428],
[ 0.5500626 , -0.12745889, -0.28661036, 0.16889155, 0.48200938],
[ 0.6217582 , -0.10251168, -0.0713837 , 0.13550574, 0.60422456],
[ 0.9797377 , -0.46282482, -0.09380057, 0.2749968 , 0.7020155 ],
[ 0.38830167, -0.30841848, -0.20950563, -0.02130592, 0.0836651 ],
[ 0.57225037, -0.04150235, -0.1246101 , 0.06918757, 0.23083903],
[ 0.6431406 , -0.04898892, -0.05708801, 0.1311793 , 0.46377632],
[ 0.541667 , -0.16031542, -0.33119023, 0.10385639, 0.39525154],
[ 0.65543544, -0.27534947, -0.28757 , 0.2080029 , 0.5288213 ],
[ 0.46381798, -0.07729273, -0.09209982, 0.11292508, 0.36836028],
[ 0.53826964, -0.09915172, -0.09243581, 0.15036733, 0.5449071 ],
[ 0.31599265, -0.22078821, -0.02872767, 0.07436654, 0.28573534],
[ 1.0706906 , -0.27783617, -0.16653039, 0.2631594 , 0.6408689 ],
[ 0.67875004, -0.34441757, -0.10262538, 0.2588695 , 0.38405937],
[ 0.41786563, -0.10344986, -0.19508548, 0.19657765, 0.22006002],
[ 0.7855942 , -0.27200857, 0.02204541, 0.09168041, 0.42220354],
[ 0.7773458 , -0.11727296, -0.24145149, 0.04537854, 0.5737133 ],
[ 0.75732976, -0.314953 , -0.15383345, 0.02065313, 0.51843405],
[ 0.7226543 , -0.31919608, -0.18878649, 0.15413427, 0.42012522],
[ 0.43411565, -0.17342259, -0.28042233, 0.26853496, 0.49947587],
[ 1.1565564 , -0.36802933, -0.12613232, 0.32381424, 0.75113887],
[ 1.1192797 , -0.162529 , -0.17195942, 0.39265418, 0.83656436],
[ 1.2231556 , -0.5336606 , -0.14015286, 0.14054438, 0.5695296 ]],
dtype=float32)