Merge febf8923f6 into d6ff7bb78c

graphiti_core/utils/maintenance/community_operations.py

@@ -84,50 +84,108 @@ async def get_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
+    Implement the label propagation community detection algorithm.
-    # 2. Each node will take on the community of the plurality of its neighbors
+
-    # 3. Ties are broken by going to the largest community
+    Algorithm:
-    # 4. Continue until no communities change during propagation
+    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
+
+    Oscillation prevention:
+    - Uses asynchronous updates (randomized node order)
+    - Maximum iteration limit to prevent infinite loops
+    - Early stopping if oscillation is detected
+    """
+    import random
+
+    MAX_ITERATIONS = 100
+    OSCILLATION_CHECK_WINDOW = 5
 
     community_map = {uuid: i for i, uuid in enumerate(projection.keys())}
+    node_uuids = list(projection.keys())
 
-    while True:
+    # Track history to detect oscillations
-        no_change = True
+    history: list[dict[str, int]] = []
-        new_community_map: dict[str, int] = {}
 
-        for uuid, neighbors in projection.items():
+    for iteration in range(MAX_ITERATIONS):
+        # Asynchronous update: randomize node processing order to prevent oscillation
+        random.shuffle(node_uuids)
+
+        changed_count = 0
+
+        for uuid in node_uuids:
+            neighbors = projection[uuid]
             curr_community = community_map[uuid]
 
+            # Count votes from neighbors
             community_candidates: dict[int, int] = defaultdict(int)
             for neighbor in neighbors:
                 community_candidates[community_map[neighbor.node_uuid]] += neighbor.edge_count
+
+            if not community_candidates:
+                continue
+
+            # Sort by count (descending), then by community ID for deterministic tie-breaking
             community_lst = [
                 (count, community) for community, count in community_candidates.items()
             ]
+            community_lst.sort(key=lambda x: (-x[0], x[1]))
 
-            community_lst.sort(reverse=True)
+            candidate_rank, community_candidate = community_lst[0]
-            candidate_rank, community_candidate = community_lst[0] if community_lst else (0, -1)
+
-            if community_candidate != -1 and candidate_rank > 1:
+            # Determine new community:
+            # - If strong signal (edge count > 1), adopt the neighbor's community
+            # - Otherwise, prefer the larger community ID (original behavior)
+            if candidate_rank > 1:
                 new_community = community_candidate
             else:
                 new_community = max(community_candidate, curr_community)
 
-            new_community_map[uuid] = new_community
-
             if new_community != curr_community:
-                no_change = False
+                community_map[uuid] = new_community
+                changed_count += 1
 
-        if no_change:
+        # Check for convergence
+        if changed_count == 0:
+            logger.debug(f'Label propagation converged after {iteration + 1} iterations')
             break
 
-        community_map = new_community_map
+        # Check for oscillation by comparing with recent history
+        current_state = community_map.copy()
+        history.append(current_state)
 
-    community_cluster_map = defaultdict(list)
+        # Keep only recent history
+        if len(history) > OSCILLATION_CHECK_WINDOW:
+            history.pop(0)
+
+        # Detect oscillation: if current state matches any recent state
+        if len(history) >= 2:
+            for past_state in history[:-1]:
+                if past_state == current_state:
+                    logger.warning(
+                        f'Label propagation oscillation detected at iteration {iteration + 1}, '
+                        'stopping early'
+                    )
+                    # Break out of the for loop
+                    break
+            else:
+                # No oscillation detected, continue to next iteration
+                continue
+            # Oscillation detected, break out of the main loop
+            break
+    else:
+        logger.warning(
+            f'Label propagation reached maximum iterations ({MAX_ITERATIONS}) without converging'
+        )
+
+    # Group nodes by community
+    community_cluster_map: dict[int, list[str]] = defaultdict(list)
     for uuid, community in community_map.items():
         community_cluster_map[community].append(uuid)
 
-    clusters = [cluster for cluster in community_cluster_map.values()]
+    clusters = list(community_cluster_map.values())
     return clusters