SNAP图摘要

这是一个基于节点属性和边属性使用节点属性与成对边分组汇总（SNAP）算法（不要与斯坦福网络分析项目混淆）来汇总图的示例。该算法通过节点属性值的唯一组合以及与其他节点组的边类型来分组节点，从而生成一个摘要图。然后，可以使用该摘要图来推断具有不同属性值的节点如何与图中的其他节点相关联。

import networkx as nx
import matplotlib.pyplot as plt


nodes = {
    "A": {"color": "Red"},
    "B": {"color": "Red"},
    "C": {"color": "Red"},
    "D": {"color": "Red"},
    "E": {"color": "Blue"},
    "F": {"color": "Blue"},
    "G": {"color": "Blue"},
    "H": {"color": "Blue"},
    "I": {"color": "Yellow"},
    "J": {"color": "Yellow"},
    "K": {"color": "Yellow"},
    "L": {"color": "Yellow"},
}
edges = [
    ("A", "B", "Strong"),
    ("A", "C", "Weak"),
    ("A", "E", "Strong"),
    ("A", "I", "Weak"),
    ("B", "D", "Weak"),
    ("B", "J", "Weak"),
    ("B", "F", "Strong"),
    ("C", "G", "Weak"),
    ("D", "H", "Weak"),
    ("I", "J", "Strong"),
    ("J", "K", "Strong"),
    ("I", "L", "Strong"),
]
original_graph = nx.Graph()
original_graph.add_nodes_from(n for n in nodes.items())
original_graph.add_edges_from((u, v, {"type": label}) for u, v, label in edges)


plt.suptitle("SNAP Summarization")

base_options = {"with_labels": True, "edgecolors": "black", "node_size": 500}

ax1 = plt.subplot(1, 2, 1)
plt.title(
    f"Original ({original_graph.number_of_nodes()} nodes, {original_graph.number_of_edges()} edges)"
)
pos = nx.spring_layout(original_graph, seed=7482934)
node_colors = [d["color"] for _, d in original_graph.nodes(data=True)]

edge_type_visual_weight_lookup = {"Weak": 1.0, "Strong": 3.0}
edge_weights = [
    edge_type_visual_weight_lookup[d["type"]]
    for _, _, d in original_graph.edges(data=True)
]

nx.draw_networkx(
    original_graph, pos=pos, node_color=node_colors, width=edge_weights, **base_options
)

node_attributes = ("color",)
edge_attributes = ("type",)
summary_graph = nx.snap_aggregation(
    original_graph, node_attributes, edge_attributes, prefix="S-"
)

plt.subplot(1, 2, 2)

plt.title(
    f"SNAP Aggregation ({summary_graph.number_of_nodes()} nodes, {summary_graph.number_of_edges()} edges)"
)
summary_pos = nx.spring_layout(summary_graph, seed=8375428)
node_colors = []
for node in summary_graph:
    color = summary_graph.nodes[node]["color"]
    node_colors.append(color)

edge_weights = []
for edge in summary_graph.edges():
    edge_types = summary_graph.get_edge_data(*edge)["types"]
    edge_weight = 0.0
    for edge_type in edge_types:
        edge_weight += edge_type_visual_weight_lookup[edge_type["type"]]
    edge_weights.append(edge_weight)

nx.draw_networkx(
    summary_graph,
    pos=summary_pos,
    node_color=node_colors,
    width=edge_weights,
    **base_options,
)

plt.tight_layout()
plt.show()