feat: 统一人设树和关系图的交互逻辑

- 添加第四层关系图（Ego Graph）显示点击节点的相关节点和边
- 提取共享函数 handleNodeClick/handleEdgeClick，人设树和关系图复用
- 统一节点样式：实心填充维度颜色，高亮不变粗
- 统一边样式：全部实线，高亮只改透明度不变粗
- 点击前先重置所有高亮状态，避免残留
- 点击任意空白区域同时取消人设树和关系图高亮
- 移除"包含"边（与"属于"重复）

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

script/data_processing/build_persona_tree.py

@@ -0,0 +1,187 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+构建人设树的中间数据
+
+输入：节点列表.json, 边关系.json
+输出：persona_tree.json（包含分类和标签的层级树结构）
+"""
+
+import json
+from pathlib import Path
+import sys
+
+# 添加项目根目录到路径
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from script.data_processing.path_config import PathConfig
+
+
+def build_persona_tree():
+    """构建人设树数据"""
+    config = PathConfig()
+
+    print(f"账号: {config.account_name}")
+    print(f"输出版本: {config.output_version}")
+    print()
+
+    node_list_file = config.intermediate_dir / "节点列表.json"
+    edge_list_file = config.intermediate_dir / "边关系.json"
+    output_file = config.intermediate_dir / "persona_tree.json"
+
+    # 读取节点
+    print(f"读取节点列表: {node_list_file.name}")
+    with open(node_list_file, "r", encoding="utf-8") as f:
+        node_data = json.load(f)
+
+    all_nodes = node_data.get("节点列表", [])
+
+    # 分离分类和标签
+    category_nodes = [n for n in all_nodes if n.get("节点类型") == "分类"]
+    tag_nodes = [n for n in all_nodes if n.get("节点类型") == "标签"]
+
+    print(f"  分类节点: {len(category_nodes)}")
+    print(f"  标签节点: {len(tag_nodes)}")
+
+    # 读取边关系（获取所有边）
+    print(f"读取边关系: {edge_list_file.name}")
+    with open(edge_list_file, "r", encoding="utf-8") as f:
+        edge_data = json.load(f)
+
+    all_edges = edge_data.get("边列表", [])
+
+    # 统计各类型边
+    edge_type_counts = {}
+    for e in all_edges:
+        t = e.get("边类型", "未知")
+        edge_type_counts[t] = edge_type_counts.get(t, 0) + 1
+
+    for t, count in sorted(edge_type_counts.items(), key=lambda x: -x[1]):
+        print(f"  {t}: {count}")
+
+    # 构建树结构
+    tree_nodes = []
+    tree_edges = []
+
+    # 添加分类节点
+    for n in category_nodes:
+        tree_nodes.append({
+            "节点ID": n["节点ID"],
+            "节点名称": n["节点名称"],
+            "节点类型": "分类",
+            "节点层级": n.get("节点层级", ""),
+            "所属分类": n.get("所属分类", []),
+            "帖子数": n.get("帖子数", 0)
+        })
+
+    # 添加标签节点
+    for n in tag_nodes:
+        tree_nodes.append({
+            "节点ID": n["节点ID"],
+            "节点名称": n["节点名称"],
+            "节点类型": "标签",
+            "节点层级": n.get("节点层级", ""),
+            "所属分类": n.get("所属分类", []),
+            "帖子数": n.get("帖子数", 0)
+        })
+
+    # 构建节点ID集合和名称映射
+    node_ids = set(n["节点ID"] for n in tree_nodes)
+
+    # 按层级构建分类名称到ID的映射
+    category_name_to_id = {}
+    for n in category_nodes:
+        level = n.get("节点层级", "")
+        name = n.get("节点名称", "")
+        category_name_to_id[(level, name)] = n["节点ID"]
+
+    # 从分类的"所属分类"字段构建分类之间的层级边（统一用"属于"）
+    for n in category_nodes:
+        level = n.get("节点层级", "")
+        parent_names = n.get("所属分类", [])
+        if parent_names:
+            parent_name = parent_names[-1]  # 取最后一个作为直接父分类
+            parent_id = category_name_to_id.get((level, parent_name))
+            if parent_id:
+                tree_edges.append({
+                    "源节点ID": n["节点ID"],
+                    "目标节点ID": parent_id,
+                    "边类型": "属于"
+                })
+
+    # 添加所有原始边（两端节点都在树中的，排除"包含"边因为与"属于"重复）
+    for e in all_edges:
+        src_id = e["源节点ID"]
+        tgt_id = e["目标节点ID"]
+        edge_type = e["边类型"]
+        # 跳过"包含"边（与"属于"是反向关系，保留"属于"即可）
+        if edge_type == "包含":
+            continue
+        if src_id in node_ids and tgt_id in node_ids:
+            tree_edges.append({
+                "源节点ID": src_id,
+                "目标节点ID": tgt_id,
+                "边类型": edge_type,
+                "边详情": e.get("边详情", {})
+            })
+
+    # 从标签的"所属分类"字段补充标签->分类的边（如果不存在）
+    for n in tag_nodes:
+        level = n.get("节点层级", "")
+        parent_names = n.get("所属分类", [])
+        if parent_names:
+            parent_name = parent_names[-1]
+            parent_id = category_name_to_id.get((level, parent_name))
+            if parent_id:
+                # 检查是否已存在属于边
+                edge_exists = any(
+                    e["源节点ID"] == n["节点ID"] and e["目标节点ID"] == parent_id
+                    and e["边类型"] == "属于"
+                    for e in tree_edges
+                )
+                if not edge_exists:
+                    tree_edges.append({
+                        "源节点ID": n["节点ID"],
+                        "目标节点ID": parent_id,
+                        "边类型": "属于",
+                        "边详情": {}
+                    })
+
+    # 统计各类型边
+    tree_edge_counts = {}
+    for e in tree_edges:
+        t = e["边类型"]
+        tree_edge_counts[t] = tree_edge_counts.get(t, 0) + 1
+
+    print()
+    print(f"构建人设树:")
+    print(f"  总节点数: {len(tree_nodes)}")
+    print(f"  总边数: {len(tree_edges)}")
+    for t, count in sorted(tree_edge_counts.items(), key=lambda x: -x[1]):
+        print(f"    {t}: {count}")
+
+    # 输出
+    output_data = {
+        "说明": {
+            "描述": "人设树结构数据（包含分类、标签和所有边类型）",
+            "分类节点数": len(category_nodes),
+            "标签节点数": len(tag_nodes),
+            "总边数": len(tree_edges),
+            "边类型统计": tree_edge_counts
+        },
+        "nodes": tree_nodes,
+        "edges": tree_edges
+    }
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(output_data, f, ensure_ascii=False, indent=2)
+
+    print()
+    print(f"输出文件: {output_file}")
+
+    return output_file
+
+
+if __name__ == "__main__":
+    build_persona_tree()