In memory label propagation community detection

README.md

@@ -2,7 +2,6 @@
 
 <img width="350" alt="Graphiti-ts-small" src="https://github.com/user-attachments/assets/bbd02947-e435-4a05-b25a-bbbac36d52c8">
 
-
 ## Temporal Knowledge Graphs for Agentic Applications
 
 <br />
@@ -80,7 +79,6 @@ Requirements:
 
 - Python 3.10 or higher
 - Neo4j 5.21 or higher
-- Neo4j GraphDataScience Plugin (required for community flows)
 - OpenAI API key (for LLM inference and embedding)
 
 Optional:

examples/podcast/podcast_runner.py

@@ -63,7 +63,7 @@ async def main(use_bulk: bool = True):
     messages = parse_podcast_messages()
 
     if not use_bulk:
-        for i, message in enumerate(messages[3:14]):
+        for i, message in enumerate(messages[3:4]):
             await client.add_episode(
                 name=f'Message {i}',
                 episode_body=f'{message.speaker_name} ({message.role}): {message.content}',
@@ -76,15 +76,15 @@ async def main(use_bulk: bool = True):
         await client.build_communities()
 
         # add additional messages to update communities
-        for i, message in enumerate(messages[14:20]):
-            await client.add_episode(
-                name=f'Message {i}',
-                episode_body=f'{message.speaker_name} ({message.role}): {message.content}',
-                reference_time=message.actual_timestamp,
-                source_description='Podcast Transcript',
-                group_id='1',
-                update_communities=True,
-            )
+        # for i, message in enumerate(messages[14:20]):
+        #     await client.add_episode(
+        #         name=f'Message {i}',
+        #         episode_body=f'{message.speaker_name} ({message.role}): {message.content}',
+        #         reference_time=message.actual_timestamp,
+        #         source_description='Podcast Transcript',
+        #         group_id='1',
+        #         update_communities=True,
+        #     )
 
         return
 

graphiti_core/graphiti.py

@@ -579,7 +579,7 @@ async def search(
         center_node_uuid: str | None = None,
         group_ids: list[str | None] | None = None,
         num_results=DEFAULT_SEARCH_LIMIT,
-    ):
+    ) -> list[EntityEdge]:
         """
         Perform a hybrid search on the knowledge graph.
 

graphiti_core/utils/maintenance/community_operations.py

@@ -4,6 +4,7 @@
 from datetime import datetime
 
 from neo4j import AsyncDriver
+from pydantic import BaseModel
 
 from graphiti_core.edges import CommunityEdge
 from graphiti_core.llm_client import LLMClient
@@ -14,6 +15,11 @@
 logger = logging.getLogger(__name__)
 
 
+class Neighbor(BaseModel):
+    node_uuid: str
+    edge_count: int
+
+
 async def build_community_projection(driver: AsyncDriver) -> str:
     records, _, _ = await driver.execute_query("""
     CALL gds.graph.project("communities", "Entity",             
@@ -29,36 +35,96 @@ async def build_community_projection(driver: AsyncDriver) -> str:
     return records[0]['graph']
 
 
-async def destroy_projection(driver: AsyncDriver, projection_name: str):
-    await driver.execute_query(
-        """
-    CALL gds.graph.drop($projection_name)
-    """,
-        projection_name=projection_name,
-    )
-
+async def get_community_clusters(driver: AsyncDriver) -> list[list[EntityNode]]:
+    community_clusters: list[list[EntityNode]] = []
 
-async def get_community_clusters(
-    driver: AsyncDriver, projection_name: str
-) -> list[list[EntityNode]]:
-    records, _, _ = await driver.execute_query("""
-    CALL gds.leiden.stream("communities")
-    YIELD nodeId, communityId
-    RETURN gds.util.asNode(nodeId).uuid AS entity_uuid, communityId
+    group_id_values, _, _ = await driver.execute_query("""
+    MATCH (n:Entity WHERE n.group_id IS NOT NULL)
+    RETURN 
+        collect(DISTINCT n.group_id) AS group_ids
     """)
-    community_map: dict[int, list[str]] = defaultdict(list)
-    for record in records:
-        community_map[record['communityId']].append(record['entity_uuid'])
 
-    community_clusters: list[list[EntityNode]] = list(
-        await asyncio.gather(
-            *[EntityNode.get_by_uuids(driver, cluster) for cluster in community_map.values()]
+    group_ids = group_id_values[0]['group_ids']
+    for group_id in group_ids:
+        projection: dict[str, list[Neighbor]] = {}
+        nodes = await EntityNode.get_by_group_ids(driver, [group_id])
+        for node in nodes:
+            records, _, _ = await driver.execute_query(
+                """
+            MATCH (n:Entity {group_id: $group_id, uuid: $uuid})-[r:RELATES_TO]-(m: Entity {group_id: $group_id})
+            WITH count(r) AS count, m.uuid AS uuid
+            RETURN
+                uuid,
+                count
+            """,
+                uuid=node.uuid,
+                group_id=group_id,
+            )
+
+            projection[node.uuid] = [
+                Neighbor(node_uuid=record['uuid'], edge_count=record['count']) for record in records
+            ]
+
+        cluster_uuids = label_propagation(projection)
+
+        community_clusters.extend(
+            list(
+                await asyncio.gather(
+                    *[EntityNode.get_by_uuids(driver, cluster) for cluster in cluster_uuids]
+                )
+            )
         )
-    )
 
     return community_clusters
 
 
+def label_propagation(projection: dict[str, list[Neighbor]]) -> list[list[str]]:
+    # Implement the label propagation community detection algorithm.
+    # 1. Start with each node being assigned its own community
+    # 2. Each node will take on the community of the plurality of its neighbors
+    # 3. Ties are broken by going to the largest community
+    # 4. Continue until no communities change during propagation
+
+    community_map = {uuid: i for i, uuid in enumerate(projection.keys())}
+
+    while True:
+        no_change = True
+        new_community_map: dict[str, int] = {}
+
+        for uuid, neighbors in projection.items():
+            curr_community = community_map[uuid]
+
+            community_candidates: dict[int, int] = defaultdict(int)
+            for neighbor in neighbors:
+                community_candidates[community_map[neighbor.node_uuid]] += neighbor.edge_count
+
+            community_lst = [
+                (count, community) for community, count in community_candidates.items()
+            ]
+
+            community_lst.sort(reverse=True)
+            community_candidate = community_lst[0][1] if len(community_lst) > 0 else -1
+
+            new_community = max(community_candidate, curr_community)
+
+            new_community_map[uuid] = new_community
+
+            if new_community != curr_community:
+                no_change = False
+
+        if no_change:
+            break
+
+        community_map = new_community_map
+
+    community_cluster_map = defaultdict(list)
+    for uuid, community in community_map.items():
+        community_cluster_map[community].append(uuid)
+
+    clusters = [cluster for cluster in community_cluster_map.values()]
+    return clusters
+
+
 async def summarize_pair(llm_client: LLMClient, summary_pair: tuple[str, str]) -> str:
     # Prepare context for LLM
     context = {'node_summaries': [{'summary': summary} for summary in summary_pair]}
@@ -85,7 +151,7 @@ async def generate_summary_description(llm_client: LLMClient, summary: str) -> s
 
 
 async def build_community(
-    llm_client: LLMClient, community_cluster: list[EntityNode]
+        llm_client: LLMClient, community_cluster: list[EntityNode]
 ) -> tuple[CommunityNode, list[CommunityEdge]]:
     summaries = [entity.summary for entity in community_cluster]
     length = len(summaries)
@@ -99,7 +165,7 @@ async def build_community(
                 *[
                     summarize_pair(llm_client, (str(left_summary), str(right_summary)))
                     for left_summary, right_summary in zip(
-                        summaries[: int(length / 2)], summaries[int(length / 2) :]
+                        summaries[: int(length / 2)], summaries[int(length / 2):]
                     )
                 ]
             )
@@ -127,10 +193,9 @@ async def build_community(
 
 
 async def build_communities(
-    driver: AsyncDriver, llm_client: LLMClient
+        driver: AsyncDriver, llm_client: LLMClient
 ) -> tuple[list[CommunityNode], list[CommunityEdge]]:
-    projection = await build_community_projection(driver)
-    community_clusters = await get_community_clusters(driver, projection)
+    community_clusters = await get_community_clusters(driver)
 
     communities: list[tuple[CommunityNode, list[CommunityEdge]]] = list(
         await asyncio.gather(
@@ -144,7 +209,6 @@ async def build_communities(
         community_nodes.append(community[0])
         community_edges.extend(community[1])
 
-    await destroy_projection(driver, projection)
     return community_nodes, community_edges
 
 
@@ -156,7 +220,7 @@ async def remove_communities(driver: AsyncDriver):
 
 
 async def determine_entity_community(
-    driver: AsyncDriver, entity: EntityNode
+        driver: AsyncDriver, entity: EntityNode
 ) -> tuple[CommunityNode | None, bool]:
     # Check if the node is already part of a community
     records, _, _ = await driver.execute_query(
@@ -217,7 +281,7 @@ async def determine_entity_community(
 
 
 async def update_community(
-    driver: AsyncDriver, llm_client: LLMClient, embedder, entity: EntityNode
+        driver: AsyncDriver, llm_client: LLMClient, embedder, entity: EntityNode
 ):
     community, is_new = await determine_entity_community(driver, entity)
 

tests/test_graphiti_int.py

@@ -74,6 +74,7 @@ def format_context(facts):
 async def test_graphiti_init():
     logger = setup_logging()
     graphiti = Graphiti(NEO4J_URI, NEO4j_USER, NEO4j_PASSWORD)
+    await graphiti.build_communities()
 
     edges = await graphiti.search('tania tetlow', group_ids=['1'])