# -*- coding: utf-8 -*-
"""
知识图谱可视化模块
使用 pyvis 生成交互式网络图,支持嵌入 Streamlit
"""
import logging
import tempfile
from pathlib import Path
from typing import Optional
import networkx as nx
logger = logging.getLogger(__name__)
# 节点类型对应的颜色和形状
NODE_STYLES = {
"科创企业": {"color": "#4CAF50", "shape": "dot", "size": 30},
"供应商": {"color": "#FF9800", "shape": "diamond", "size": 20},
"客户": {"color": "#9C27B0", "shape": "triangle", "size": 20},
"核心人员": {"color": "#2196F3", "shape": "star", "size": 15},
"投资方": {"color": "#607D8B", "shape": "square", "size": 20},
"企业": {"color": "#4CAF50", "shape": "dot", "size": 25},
}
# 受制裁节点的样式覆盖
SANCTIONED_STYLE = {"color": "#F44336", "size": 35}
# 受限边的样式
RESTRICTED_EDGE_STYLE = {"color": "#F44336", "dashes": True, "width": 3}
def generate_interactive_graph(
G: nx.DiGraph,
highlight_company: Optional[str] = None,
output_path: Optional[str] = None,
height: str = "600px",
width: str = "100%",
) -> str:
"""
生成交互式知识图谱 HTML
"""
try:
from pyvis.network import Network
except ImportError:
logger.error("pyvis 未安装,请运行: pip install pyvis")
return _generate_fallback_html(G)
net = Network(
height=height,
width=width,
directed=True,
notebook=False,
bgcolor="#1a1a2e",
font_color="white",
)
# 物理引擎配置
net.set_options("""
{
"physics": {
"forceAtlas2Based": {
"gravitationalConstant": -50,
"centralGravity": 0.01,
"springLength": 150,
"springConstant": 0.08
},
"solver": "forceAtlas2Based",
"stabilization": {"iterations": 100}
},
"interaction": {
"hover": true,
"tooltipDelay": 200,
"navigationButtons": true
}
}
""")
# 添加节点
for node, data in G.nodes(data=True):
node_type = data.get("node_type", "企业")
style = NODE_STYLES.get(node_type, NODE_STYLES["企业"]).copy()
# 受制裁节点特殊样式
if data.get("is_sanctioned", False):
style.update(SANCTIONED_STYLE)
# 高亮选中企业
if highlight_company and node == highlight_company:
style["color"] = "#FFD700" # 金色
style["size"] = 45
style["borderWidth"] = 3
# 构建标签和悬浮提示
label = node.split("@")[0] if "@" in node else node
title_parts = [f"{label}", f"类型: {node_type}"]
if data.get("is_sanctioned"):
title_parts.append("⚠️ 已被制裁")
if data.get("stock_code"):
title_parts.append(f"代码: {data['stock_code']}")
if data.get("industry"):
title_parts.append(f"行业: {data['industry']}")
if data.get("title"):
title_parts.append(f"职位: {data['title']}")
if data.get("status"):
title_parts.append(f"状态: {data['status']}")
net.add_node(
node,
label=label,
title="
".join(title_parts),
color=style["color"],
shape=style["shape"],
size=style["size"],
)
# 添加边
for u, v, data in G.edges(data=True):
edge_style = {
"color": "#666666",
"width": 1,
"arrows": "to",
}
# 受限边特殊样式
if data.get("status") == "受限":
edge_style.update(RESTRICTED_EDGE_STYLE)
elif data.get("critical", False):
edge_style["width"] = 2
edge_style["color"] = "#FFC107" # 关键边用黄色
relation = data.get("relation", "")
net.add_edge(
u, v,
title=relation,
label=relation if len(relation) <= 8 else "",
**edge_style,
)
# 输出 HTML
if output_path is None:
output_path = str(Path(tempfile.gettempdir()) / "kg_visualization.html")
net.save_graph(output_path)
# 读取 HTML 内容
with open(output_path, "r", encoding="utf-8") as f:
return f.read()
def _generate_fallback_html(G: nx.DiGraph) -> str:
"""当 pyvis 不可用时的备用 HTML 可视化"""
nodes_info = []
for node, data in G.nodes(data=True):
label = node.split("@")[0] if "@" in node else node
is_sanctioned = data.get("is_sanctioned", False)
nodes_info.append(f"
节点数: {G.number_of_nodes()} | 边数: {G.number_of_edges()}
安装 pyvis 以获得交互式可视化: pip install pyvis
""" def get_subgraph_for_company(G: nx.DiGraph, company: str, depth: int = 2) -> nx.DiGraph: """ 提取以指定企业为中心的子图(上下游 N 层) """ if company not in G: return nx.DiGraph() # 收集相关节点 related_nodes = {company} # 上游(前驱) current_layer = {company} for _ in range(depth): next_layer = set() for node in current_layer: next_layer.update(G.predecessors(node)) related_nodes.update(next_layer) current_layer = next_layer # 下游(后继) current_layer = {company} for _ in range(depth): next_layer = set() for node in current_layer: next_layer.update(G.successors(node)) related_nodes.update(next_layer) current_layer = next_layer return G.subgraph(related_nodes).copy()