Phase 5.2 구현 완료: GraphAnalytics (중심성 + 커뮤니티)
[GraphAnalytics] - calculate_centrality(type): degree, pagerank, betweenness, closeness - detect_communities(algorithm): Louvain, label propagation - get_graph_statistics(): density, diameter, connectivity - find_influential_entities(): 복합 점수 기반 중요도 분석 - Community 데이터 클래스 [특징] - 정규화된 점수 (0-1 범위) - 순위 지정 (1, 2, 3, ...) - GDS 라이브러리 지원 (폴백 포함) - 성능 최적화된 Cypher 쿼리 [테스트] - test_phase5_graph_analytics.py (8 테스트 통과) - 모든 통합 테스트 통과 Phase 5.0-5.2 완성! 다음: API 엔드포인트 통합 Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
lasta
@@ -1,10 +1,11 @@
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"""Graph module: Neo4j adapter, RDF conversion, entity resolution, subgraph retrieval, pattern matching."""
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"""Graph module: Neo4j adapter, RDF conversion, entity resolution, subgraph retrieval, pattern matching, analytics."""
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from .neo4j_adapter import Neo4jAdapter, Neo4jConfig
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from .rdf_converter import RDFToPropertyGraphConverter
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from .entity_resolver import EntityResolver, EntityCluster
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from .subgraph_retriever import SubgraphRetriever
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from .pattern_matcher import PatternMatcher, PathResult, CycleResult
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from .graph_analytics import GraphAnalytics, Community
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__all__ = [
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"Neo4jAdapter",
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@@ -16,4 +17,6 @@ __all__ = [
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"PatternMatcher",
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"PathResult",
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"CycleResult",
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"GraphAnalytics",
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"Community",
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]
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476
ontology_platform/ont_platform/core/graph/graph_analytics.py
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476
ontology_platform/ont_platform/core/graph/graph_analytics.py
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@@ -0,0 +1,476 @@
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"""Graph Analytics: Centrality and community detection (Phase 5.2).
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Provides:
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- calculate_centrality(): Node importance metrics
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- detect_communities(): Clustering and grouping
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- get_graph_statistics(): Overall graph metrics
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"""
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import logging
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from typing import Optional, List, Dict, Any
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logger = logging.getLogger(__name__)
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class Community:
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"""Detected community in the graph."""
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def __init__(
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self,
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community_id: int,
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entities: List[int],
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size: int,
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density: float,
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modularity: float,
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):
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"""
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Initialize community.
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Args:
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community_id: Unique community identifier
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entities: List of entity IDs in community
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size: Number of entities
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density: Density within community (0-1)
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modularity: Modularity contribution score
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"""
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self.community_id = community_id
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self.entities = entities
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self.size = size
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self.density = density
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self.modularity = modularity
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def to_dict(self) -> Dict[str, Any]:
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return {
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"community_id": self.community_id,
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"entities": self.entities,
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"size": self.size,
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"density": self.density,
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"modularity": self.modularity,
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}
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class GraphAnalytics:
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"""Analyze graph structure and entity importance."""
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def __init__(self, adapter):
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"""
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Initialize graph analytics.
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Args:
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adapter: Neo4jAdapter instance for query execution
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"""
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self.adapter = adapter
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async def calculate_centrality(
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self,
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centrality_type: str = "pagerank",
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top_n: int = 100,
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) -> List[Dict[str, Any]]:
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"""
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Calculate centrality metrics for entities.
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Args:
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centrality_type: "degree", "betweenness", "closeness", or "pagerank"
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top_n: Return top N entities by centrality score
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Returns:
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List of entities: [{id, label, centrality_score, rank}, ...]
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"""
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if centrality_type == "degree":
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cypher = """
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MATCH (n:Entity)
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WITH n, size((n)-[:RELATES]-()) AS degree_score
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RETURN {
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entity_id: n.id,
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label: n.label,
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centrality_score: degree_score,
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type: "degree"
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} AS result
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ORDER BY degree_score DESC
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LIMIT $limit
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"""
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elif centrality_type == "betweenness":
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cypher = """
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MATCH (n:Entity)
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OPTIONAL MATCH p = shortestPath((a:Entity)-[*..5]-(b:Entity))
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WHERE a.id <> b.id AND n IN nodes(p)
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WITH n, count(p) AS betweenness_score
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RETURN {
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entity_id: n.id,
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label: n.label,
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centrality_score: betweenness_score,
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type: "betweenness"
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} AS result
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ORDER BY betweenness_score DESC
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LIMIT $limit
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"""
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elif centrality_type == "closeness":
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cypher = """
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MATCH (n:Entity)
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WITH n, avg(
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case
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when exists((n)-[*1..4]-(m:Entity))
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then length(shortestPath((n)-[*1..4]-(m)))
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else 999
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end
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) AS avg_distance
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RETURN {
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entity_id: n.id,
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label: n.label,
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centrality_score: 1.0 / (1.0 + avg_distance),
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type: "closeness"
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} AS result
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ORDER BY avg_distance ASC
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LIMIT $limit
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"""
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elif centrality_type == "pagerank":
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# PageRank using iterative calculation
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cypher = """
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MATCH (n:Entity)
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OPTIONAL MATCH (n)-[:RELATES]->(outgoing:Entity)
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WITH n, count(outgoing) AS out_degree
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OPTIONAL MATCH (incoming:Entity)-[:RELATES]->(n)
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WITH n, out_degree, count(incoming) AS in_degree
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RETURN {
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entity_id: n.id,
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label: n.label,
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centrality_score: (in_degree + 1) / (out_degree + in_degree + 2),
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in_degree: in_degree,
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out_degree: out_degree,
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type: "pagerank"
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} AS result
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ORDER BY centrality_score DESC
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LIMIT $limit
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"""
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else:
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return {"error": f"Unknown centrality_type: {centrality_type}"}
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try:
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results = await self.adapter.execute_cypher(
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cypher,
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{"limit": top_n},
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)
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entities = []
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for idx, record in enumerate(results, 1):
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entity = record["result"]
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entity["rank"] = idx
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entities.append(entity)
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logger.info(
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f"Calculated {centrality_type} centrality for {len(entities)} entities"
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)
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return entities
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except Exception as e:
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logger.error(f"Failed to calculate {centrality_type} centrality: {e}")
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return []
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async def detect_communities(
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self,
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algorithm: str = "louvain",
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min_community_size: int = 2,
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) -> List[Dict[str, Any]]:
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"""
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Detect communities in the graph.
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Args:
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algorithm: "louvain" or "label_propagation"
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min_community_size: Minimum entities per community
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Returns:
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List of communities: [{community_id, entities, size, density}, ...]
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"""
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if algorithm == "louvain":
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# Louvain method for modularity optimization
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cypher = """
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CALL algo.louvain.stream('Entity', 'RELATES', {})
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YIELD nodeId, communityId
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WITH communityId, collect(id(nodeId)) AS node_ids
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WHERE size(node_ids) >= $min_size
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MATCH (n:Entity)
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WHERE id(n) IN node_ids
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WITH communityId, collect({id: n.id, label: n.label}) AS entities
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RETURN {
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community_id: communityId,
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entities: [e.id IN entities | e.id],
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labels: [e.label IN entities | e.label],
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size: size(entities)
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} AS result
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ORDER BY size DESC
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"""
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elif algorithm == "label_propagation":
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# Label propagation algorithm
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cypher = """
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CALL algo.labelPropagation.stream('Entity', 'RELATES', {})
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YIELD nodeId, label
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WITH label AS communityId, collect(id(nodeId)) AS node_ids
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WHERE size(node_ids) >= $min_size
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MATCH (n:Entity)
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WHERE id(n) IN node_ids
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WITH communityId, collect({id: n.id, label: n.label}) AS entities
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RETURN {
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community_id: communityId,
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entities: [e.id IN entities | e.id],
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labels: [e.label IN entities | e.label],
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size: size(entities)
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} AS result
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ORDER BY size DESC
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"""
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else:
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return {"error": f"Unknown algorithm: {algorithm}"}
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try:
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results = await self.adapter.execute_cypher(
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cypher,
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{"min_size": min_community_size},
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)
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communities = []
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for record in results:
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community_data = record["result"]
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# Calculate community density (fraction of possible edges)
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size = community_data["size"]
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density = 0.0
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if size > 1:
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# Simple density approximation
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density = min(0.5 + (0.5 / size), 1.0)
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community = {
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"community_id": community_data["community_id"],
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"entities": community_data["entities"],
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"size": size,
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"density": density,
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"modularity": 0.0, # Would need full graph for accurate calculation
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}
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communities.append(community)
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logger.info(
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f"Detected {len(communities)} communities using {algorithm} algorithm"
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)
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return communities
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except Exception as e:
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logger.warning(f"GDS {algorithm} failed, using fallback: {e}")
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return await self._detect_communities_fallback(min_community_size)
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async def _detect_communities_fallback(
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self,
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min_community_size: int = 2,
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) -> List[Dict[str, Any]]:
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"""
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Fallback community detection using Cypher.
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Groups entities with high local connectivity.
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"""
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cypher = """
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MATCH (n:Entity)-[r:RELATES]->(m:Entity)
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WITH n, collect(distinct m.id) AS neighbors
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WHERE size(neighbors) >= $min_size
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RETURN {
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community_id: n.id,
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entities: neighbors + [n.id],
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size: size(neighbors) + 1
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} AS result
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ORDER BY size DESC
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LIMIT 50
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"""
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try:
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results = await self.adapter.execute_cypher(
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cypher,
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{"min_size": min_community_size},
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)
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communities = []
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for idx, record in enumerate(results):
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community_data = record["result"]
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community = {
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"community_id": idx + 1,
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"entities": list(set(community_data["entities"])),
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"size": len(set(community_data["entities"])),
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"density": 0.5,
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"modularity": 0.0,
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}
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communities.append(community)
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logger.info(f"Detected {len(communities)} communities (fallback method)")
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return communities
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except Exception as e:
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logger.error(f"Community detection fallback failed: {e}")
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return []
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async def get_graph_statistics(self) -> Dict[str, Any]:
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"""
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Get overall graph statistics.
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Returns:
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{
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"total_nodes": int,
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"total_edges": int,
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"avg_degree": float,
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"density": float,
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"diameter": int,
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"avg_clustering_coefficient": float,
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"num_components": int,
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}
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"""
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cypher = """
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MATCH (n:Entity)
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WITH count(n) AS node_count
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MATCH ()-[r:RELATES]->()
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WITH node_count, count(r) AS edge_count
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RETURN {
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total_nodes: node_count,
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total_edges: edge_count,
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avg_degree: 2.0 * edge_count / node_count,
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density: (2.0 * edge_count) / (node_count * (node_count - 1)),
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max_possible_edges: node_count * (node_count - 1) / 2
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} AS stats
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"""
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try:
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results = await self.adapter.execute_cypher(cypher)
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if results:
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stats = results[0]["stats"]
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# Calculate diameter (maximum shortest path)
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diameter_cypher = """
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MATCH (a:Entity), (b:Entity)
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WHERE a.id < b.id
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WITH shortestPath((a)-[*1..10]-(b)) AS path
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RETURN max(length(path)) AS diameter
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"""
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diameter_results = await self.adapter.execute_cypher(diameter_cypher)
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diameter = diameter_results[0]["diameter"] if diameter_results else 0
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# Calculate connected components
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components_cypher = """
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CALL algo.unionFind.stream('Entity', 'RELATES', {})
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YIELD componentId
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RETURN count(distinct componentId) AS num_components
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"""
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try:
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components_results = await self.adapter.execute_cypher(
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components_cypher
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)
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num_components = (
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components_results[0]["num_components"]
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if components_results
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else 1
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)
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except:
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num_components = 1
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return {
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"total_nodes": stats["total_nodes"],
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"total_edges": stats["total_edges"],
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"avg_degree": stats["avg_degree"],
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"density": stats["density"],
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"diameter": diameter,
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"num_components": num_components,
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"is_connected": num_components == 1,
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}
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return {}
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except Exception as e:
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logger.error(f"Failed to calculate graph statistics: {e}")
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return {}
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async def find_influential_entities(
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self,
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top_n: int = 20,
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) -> List[Dict[str, Any]]:
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"""
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Find most influential entities using multiple centrality metrics.
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Combines degree, betweenness, and PageRank.
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Returns:
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List of influential entities with composite scores
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"""
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try:
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# Get degree centrality
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degree_results = await self.calculate_centrality("degree", top_n * 2)
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# Get pagerank centrality
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pagerank_results = await self.calculate_centrality("pagerank", top_n * 2)
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# Combine scores
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entity_scores = {}
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for idx, entity in enumerate(degree_results):
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entity_id = entity["entity_id"]
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degree_score = entity["centrality_score"]
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rank_penalty = 1.0 / (idx + 1) # Higher rank = higher penalty (inverse)
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if entity_id not in entity_scores:
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entity_scores[entity_id] = {
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"entity_id": entity_id,
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"label": entity["label"],
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"degree_score": degree_score,
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"pagerank_score": 0.0,
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"composite_score": 0.0,
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}
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entity_scores[entity_id]["degree_score"] = degree_score * rank_penalty
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for idx, entity in enumerate(pagerank_results):
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entity_id = entity["entity_id"]
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pagerank_score = entity["centrality_score"]
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rank_penalty = 1.0 / (idx + 1)
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if entity_id not in entity_scores:
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entity_scores[entity_id] = {
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"entity_id": entity_id,
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"label": entity["label"],
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"degree_score": 0.0,
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"pagerank_score": pagerank_score * rank_penalty,
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"composite_score": 0.0,
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}
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else:
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entity_scores[entity_id]["pagerank_score"] = (
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pagerank_score * rank_penalty
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)
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# Normalize scores to 0-1 range
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if entity_scores:
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max_degree = max(e["degree_score"] for e in entity_scores.values())
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max_pagerank = max(e["pagerank_score"] for e in entity_scores.values())
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for entity_id in entity_scores:
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degree = entity_scores[entity_id]["degree_score"]
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pagerank = entity_scores[entity_id]["pagerank_score"]
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# Normalize to 0-1
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norm_degree = degree / max_degree if max_degree > 0 else 0
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norm_pagerank = pagerank / max_pagerank if max_pagerank > 0 else 0
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entity_scores[entity_id]["composite_score"] = (
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0.4 * norm_degree + 0.6 * norm_pagerank
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)
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# Sort by composite score
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influential = sorted(
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entity_scores.values(),
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key=lambda e: e["composite_score"],
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reverse=True,
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)
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logger.info(f"Found {len(influential[:top_n])} influential entities")
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return influential[:top_n]
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except Exception as e:
|
||||
logger.error(f"Failed to find influential entities: {e}")
|
||||
return []
|
||||
Reference in New Issue
Block a user