33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
import networkx as nx
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import matplotlib.pyplot as plt
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# 读取GraphML文件
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graphml = r"D:\dsWork\dsProject\dsLightRag\Topic\Math\graph_chunk_entity_relation.graphml"
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# graphml=r"D:\dsWork\dsProject\dsLightRag\Doc\史校长资料\math_0710-技术二\output.graphml"
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G = nx.read_graphml(graphml)
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# 绘制知识图谱
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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plt.figure(figsize=(16, 12), dpi=300) # 增加DPI
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# 使用更分散的布局算法
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pos = nx.spring_layout(G, k=0.3, iterations=50) # 增加k值和迭代次数
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# 减少节点大小并增加透明度
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nx.draw_networkx_nodes(G, pos, node_size=20, node_color="skyblue", alpha=0.7)
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nx.draw_networkx_edges(G, pos, edge_color="gray", alpha=0.3, width=0.5)
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# 只显示重要节点的标签
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labels = {}
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for n in G.nodes():
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labels[n] = G.nodes[n]['name'].split('。')[0][0:4]
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nx.draw_networkx_labels(G, pos, labels=labels, font_size=8)
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plt.title("史校长著作小学篇", fontsize=20)
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plt.axis("off")
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plt.tight_layout()
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plt.savefig('史校长著作小学篇.png', format='png', bbox_inches='tight', dpi=300) # 保存为PNG
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plt.show()
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