ForceAtlas

此笔记本演示了通过力导向算法 Force Atlas 2 对图进行嵌入。

[1]:

from IPython.display import SVG

[2]:

from sknetwork.data import karate_club
from sknetwork.embedding.force_atlas import ForceAtlas
from sknetwork.visualization import visualize_graph

示例

[3]:

graph = karate_club(metadata=True)
adjacency = graph.adjacency
labels = graph.labels

[4]:

forceatlas2 = ForceAtlas()
embedding = forceatlas2.fit_transform(adjacency)
image = visualize_graph(adjacency, embedding, labels=labels)
SVG(image)

[4]:

../../_images/tutorials_embedding_forceatlas_5_0.svg

选项

这里我们说明了提供给用户的不同设置的影响。

将线性吸引力替换为对数吸引力。

[5]:

forceatlas2 = ForceAtlas(lin_log = True)
embedding = forceatlas2.fit_transform(adjacency)
image = visualize_graph(adjacency, embedding, labels=labels)
SVG(image)

[5]:

../../_images/tutorials_embedding_forceatlas_9_0.svg

设置重力和排斥力常数（gravity_factor 和 repulsion_factor）以设置布局中每个力的重要性。保持值在 0.01 到 0.1 之间。

[6]:

forceatlas2 = ForceAtlas(gravity_factor = 0.1)
embedding = forceatlas2.fit_transform(adjacency)
image = visualize_graph(adjacency, embedding, labels=labels)
SVG(image)

[6]:

../../_images/tutorials_embedding_forceatlas_11_0.svg

设置允许的摆动量。较低的摆动幅度导致速度降低，精度提高。

[7]:

forceatlas2 = ForceAtlas(tolerance=1.5)
embedding = forceatlas2.fit_transform(adjacency)
image = visualize_graph(adjacency, embedding, labels=labels)
SVG(image)

[7]:

../../_images/tutorials_embedding_forceatlas_13_0.svg

[8]:

forceatlas2 = ForceAtlas(approx_radius=2)
embedding = forceatlas2.fit_transform(adjacency)
image = visualize_graph(adjacency, embedding, labels=labels)
SVG(image)

[8]:

../../_images/tutorials_embedding_forceatlas_14_0.svg