perf(graph): 优化图布局性能并改进节点查询算法
调整力导向图布局参数以加快收敛速度并减少节点重叠 重构节点查询逻辑，优先返回连通的子图结构 完善关系查询，确保双向关系的完整性
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Wenjie Zhang
parent
2648bec1be
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a1080a4641
@ -56,27 +56,111 @@ class GraphDatabase:
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return self.status == "open" or self.status == "processing"
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def get_sample_nodes(self, kgdb_name='neo4j', num=50):
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"""获取指定数据库的 num 个节点信息"""
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"""获取指定数据库的 num 个节点信息，优先返回连通的节点子图"""
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assert self.driver is not None, "Database is not connected"
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self.use_database(kgdb_name)
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def query(tx, num):
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"""Note: 这里只查询带有 Entity 标签的节点"""
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"""Note: 使用连通性查询获取集中的节点子图"""
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# 首先尝试获取一个连通的子图
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query_str = """
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MATCH (n:Entity)-[r]->(m:Entity)
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RETURN
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{id: elementId(n), name: n.name} AS h,
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{type: r.type, source_id: elementId(n), target_id: elementId(m)} AS r,
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{id: elementId(m), name: m.name} AS t
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LIMIT $num
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"""
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results = tx.run(query_str, num=int(num))
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formatted_results = {'nodes': [], 'edges': []}
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// 获取高度数节点作为种子节点
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MATCH (seed:Entity)
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WITH seed, COUNT{(seed)-[]->()} + COUNT{(seed)<-[]-()} as degree
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WHERE degree > 0
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ORDER BY degree DESC
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LIMIT 5
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for item in results:
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formatted_results['nodes'].extend([item['h'], item['t']])
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formatted_results['edges'].append(item['r'])
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// 为每个种子节点控制连接数量，避免过度集中
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UNWIND seed as s
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MATCH (s)-[*1..1]-(neighbor:Entity)
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WITH s, neighbor, COUNT{(s)-[]->()} + COUNT{(s)<-[]-()} as s_degree
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WITH s, s_degree, collect(neighbor) as neighbors
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WITH s, s_degree, neighbors[0..toInteger($num * 0.1)] as limited_neighbors
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return formatted_results
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// 从邻居节点扩展到二跳节点，形成开枝散叶结构
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UNWIND limited_neighbors as neighbor
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OPTIONAL MATCH (neighbor)-[*1..1]-(second_hop:Entity)
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WHERE second_hop <> s
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WITH s, limited_neighbors, neighbor, collect(DISTINCT second_hop)[0..2] as second_hops
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// 收集所有连通节点
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WITH collect(DISTINCT s) as seeds,
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collect(DISTINCT neighbor) as neighbors,
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reduce(acc = [], x IN collect(second_hops) | acc + x) as second_hop_nodes
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WITH seeds + neighbors + second_hop_nodes as connected_nodes
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// 只使用连接的节点，不添加随机节点
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WITH connected_nodes[0..$num] as final_nodes
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// 获取这些节点之间的关系，避免双向边
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UNWIND final_nodes as n
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MATCH (n)-[rel]-(m)
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WHERE m IN final_nodes AND elementId(n) < elementId(m)
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RETURN
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{id: elementId(n), name: n.name} AS h,
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{type: rel.type, source_id: elementId(n), target_id: elementId(m)} AS r,
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{id: elementId(m), name: m.name} AS t
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"""
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try:
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results = tx.run(query_str, num=int(num))
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formatted_results = {'nodes': [], 'edges': []}
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node_ids = set()
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for item in results:
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h_node = item['h']
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t_node = item['t']
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# 避免重复添加节点
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if h_node['id'] not in node_ids:
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formatted_results['nodes'].append(h_node)
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node_ids.add(h_node['id'])
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if t_node['id'] not in node_ids:
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formatted_results['nodes'].append(t_node)
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node_ids.add(t_node['id'])
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formatted_results['edges'].append(item['r'])
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# 如果连通查询没有返回足够的结果，回退到原始查询
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if len(formatted_results['nodes']) < num // 2:
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fallback_query = """
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MATCH (n:Entity)-[r]-(m:Entity)
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WHERE elementId(n) < elementId(m)
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RETURN
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{id: elementId(n), name: n.name} AS h,
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{type: r.type, source_id: elementId(n), target_id: elementId(m)} AS r,
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{id: elementId(m), name: m.name} AS t
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LIMIT $num
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"""
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fallback_results = tx.run(fallback_query, num=int(num))
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formatted_results = {'nodes': [], 'edges': []}
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for item in fallback_results:
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formatted_results['nodes'].extend([item['h'], item['t']])
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formatted_results['edges'].append(item['r'])
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return formatted_results
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except Exception as e:
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# 如果连通查询失败，使用原始查询作为备选
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logger.warning(f"Connected subgraph query failed, falling back to simple query: {e}")
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fallback_query = """
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MATCH (n:Entity)-[r]-(m:Entity)
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WHERE elementId(n) < elementId(m)
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RETURN
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{id: elementId(n), name: n.name} AS h,
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{type: r.type, source_id: elementId(n), target_id: elementId(m)} AS r,
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{id: elementId(m), name: m.name} AS t
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LIMIT $num
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"""
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results = tx.run(fallback_query, num=int(num))
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formatted_results = {'nodes': [], 'edges': []}
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for item in results:
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formatted_results['nodes'].extend([item['h'], item['t']])
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formatted_results['edges'].append(item['r'])
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return formatted_results
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with self.driver.session() as session:
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results = session.execute_read(query, num)
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@ -352,17 +436,29 @@ class GraphDatabase:
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def query(tx, entity_name, hops, limit):
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try:
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query_str = """
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MATCH (n {name: $entity_name})-[r1]-(m1)
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MATCH (n {name: $entity_name})-[r1]->(m1)
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RETURN
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{id: elementId(n), name: n.name} AS h,
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{type: r1.type, source_id: elementId(n), target_id: elementId(m1)} AS r,
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{id: elementId(m1), name: m1.name} AS t
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UNION
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MATCH (n {name: $entity_name})-[r1]-(m1)-[r2]-(m2)
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MATCH (n {name: $entity_name})-[r1]->(m1)-[r2]->(m2)
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RETURN
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{id: elementId(m1), name: m1.name} AS h,
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{type: r2.type, source_id: elementId(m1), target_id: elementId(m2)} AS r,
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{id: elementId(m2), name: m2.name} AS t
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UNION
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MATCH (m1)-[r1]->(n {name: $entity_name})
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RETURN
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{id: elementId(m1), name: m1.name} AS h,
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{type: r1.type, source_id: elementId(m1), target_id: elementId(n)} AS r,
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{id: elementId(n), name: n.name} AS t
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UNION
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MATCH (m2)-[r2]->(m1)-[r1]->(n {name: $entity_name})
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RETURN
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{id: elementId(m2), name: m2.name} AS h,
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{type: r2.type, source_id: elementId(m2), target_id: elementId(m1)} AS r,
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{id: elementId(m1), name: m1.name} AS t
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LIMIT $limit
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"""
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results = tx.run(query_str, entity_name=entity_name, limit=limit)
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@ -314,9 +314,34 @@ const initGraph = () => {
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layout: {
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type: 'd3-force',
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preventOverlap: true,
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// 优化布局性能参数
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alphaDecay: 0.08, // 增加衰减率，加快收敛速度
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alphaMin: 0.01, // 提高最小alpha值，更早停止模拟
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velocityDecay: 0.8, // 增加速度衰减，减少震荡
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iterations: 100, // 限制最大迭代次数
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// 力的配置
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force: {
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// 中心力 - 将节点拉向中心
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center: {
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x: 0.5,
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y: 0.5,
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strength: 0.1
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},
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// 排斥力 - 节点间相互排斥
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charge: {
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strength: -300, // 负值表示排斥力
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distanceMax: 400 // 限制力的作用距离
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},
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// 链接力 - 连接的节点相互吸引
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link: {
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distance: 100, // 理想链接距离
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strength: 0.8 // 链接强度
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}
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},
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collide: {
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radius: 40,
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strength: 0.5, // 碰撞强度
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strength: 0.8, // 增加碰撞强度，减少重叠
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iterations: 3 // 限制碰撞检测迭代次数
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},
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},
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node: {
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