defmodule WeightedGraphDatabase do
  @moduledoc """
  Weighted Property Graph Database using Enhanced ADT with WarpEngine Integration

  This example demonstrates how Enhanced ADT transforms complex graph database
  operations into intelligent physics-enhanced database commands. Graph structures
  become mathematical expressions that automatically leverage wormhole routing,
  quantum entanglement, and gravitational optimization.

  ## Features Demonstrated

  - **Weighted Nodes & Edges** with physics annotations
  - **Graph Traversal** using Enhanced ADT fold operations
  - **Network Generation** using bend operations with automatic wormholes
  - **Property Queries** with physics-enhanced performance
  - **Social Network Analysis** with quantum correlation enhancement
  - **Recommendation Engine** with gravitational clustering

  ## Graph Types

  - **Social Networks** - People, relationships, communities
  - **Knowledge Graphs** - Concepts, relationships, hierarchies
  - **Recommendation Networks** - Users, products, preferences
  - **Organizational Charts** - Roles, reporting, departments
  """

  use EnhancedADT
  use EnhancedADT.WarpEngineIntegration

  require Logger

  # =============================================================================
  # GRAPH ADT DEFINITIONS WITH PHYSICS ANNOTATIONS
  # =============================================================================

  @doc """
  Graph Node - Represents entities in the weighted property graph.

  Physics annotations optimize storage and access patterns:
  - importance_score → gravitational_mass (affects shard placement)
  - activity_level → quantum_entanglement_potential (for correlations)
  - created_at → temporal_weight (lifecycle management)
  """
  defproduct GraphNode do
    field id :: String.t()
    field label :: String.t()
    field properties :: map(), physics: :quantum_entanglement_group
    field importance_score :: float(), physics: :gravitational_mass
    field activity_level :: float(), physics: :quantum_entanglement_potential
    field created_at :: DateTime.t(), physics: :temporal_weight
    field node_type :: atom()
  end

  # Graph Edge - Represents weighted relationships between nodes with physics annotations
  defproduct GraphEdge do
    field id :: String.t()
    field from_node :: String.t()
    field to_node :: String.t()
    field weight :: float(), physics: :gravitational_mass
    field frequency :: float(), physics: :quantum_entanglement_potential
    field relationship_type :: atom()
    field properties :: map()
    field created_at :: DateTime.t(), physics: :temporal_weight
    field relationship_strength :: float()
  end

  # Graph Path - Represents traversal paths with physics optimization
  defproduct GraphPath do
    field id :: String.t()
    field nodes :: [String.t()]
    field edges :: [String.t()]
    field total_weight :: float(), physics: :gravitational_mass
    field traversal_frequency :: float(), physics: :quantum_entanglement_potential
    field path_efficiency :: float()
    field created_at :: DateTime.t(), physics: :temporal_weight
  end

  # Weighted Graph Structure - Sum type representing different graph topologies
  defsum WeightedGraph do
    variant EmptyGraph
    variant SingleNode, node :: GraphNode.t()
    variant ConnectedGraph,
      nodes :: [GraphNode.t()],
      edges :: [GraphEdge.t()],
      topology_type :: atom()
    variant ClusteredGraph,
      clusters :: [GraphCluster.t()],
      inter_cluster_edges :: [GraphEdge.t()],
      clustering_algorithm :: atom()
    variant HierarchicalGraph,
      root :: GraphNode.t(),
      children :: [rec(WeightedGraph)],
      hierarchy_type :: atom()
  end

  # Graph Cluster - Represents clustered subgraphs with physics optimization
  defproduct GraphCluster do
    field id :: String.t()
    field nodes :: [GraphNode.t()]
    field internal_edges :: [GraphEdge.t()]
    field cluster_weight :: float(), physics: :gravitational_mass
    field coherence_score :: float(), physics: :quantum_entanglement_potential
    field cluster_type :: atom()
  end

  # =============================================================================
  # GRAPH DATABASE OPERATIONS WITH ENHANCED ADT
  # =============================================================================

  # Store graph node with automatic physics optimization and Enhanced ADT features
  def store_node(node) do
    fold node do
      %GraphNode{id: id, label: label, properties: properties, importance_score: importance,
                activity_level: activity, created_at: created_at, node_type: node_type} ->
        # Enhanced ADT automatically translates to optimized WarpEngine operation
        node_key = "node:#{id}"

        # Physics context automatically extracted from annotations
        # gravitational_mass: importance (affects shard placement)
        # quantum_entanglement_potential: activity (correlation strength)
        # temporal_weight: created_at (lifecycle management)

        case WarpEngine.cosmic_put(node_key, node, extract_node_physics(node)) do
          {:ok, :stored, shard_id, operation_time} ->
            # Logger.info("📊 Node stored: #{id} (#{label}) in #{shard_id} shard (#{operation_time}μs)")  # Disabled for performance

            # Automatic optimizations based on node characteristics
            post_store_node_optimization(node_key, node, shard_id)

            {:ok, node_key, shard_id, operation_time}

          error ->
            Logger.error("❌ Node storage failed: #{id} - #{inspect(error)}")
            error
        end
    end
  end

  @doc """
  Store graph edge with automatic wormhole route creation.

  Enhanced ADT automatically:
  - Analyzes edge weight for wormhole route potential
  - Creates bidirectional wormhole routes for high-weight edges
  - Establishes quantum entanglements between connected nodes
  """
  def store_edge(edge) do
    fold edge do
      %GraphEdge{id: id, from_node: from_node, to_node: to_node, weight: weight,
                frequency: frequency, relationship_type: rel_type, properties: props,
                created_at: created_at, relationship_strength: strength} ->
        edge_key = "edge:#{id}"

        # Store edge with physics optimization
        case WarpEngine.cosmic_put(edge_key, edge, extract_edge_physics(edge)) do
          {:ok, :stored, shard_id, operation_time} ->
            # Logger.info("🔗 Edge stored: #{from_node} → #{to_node} (weight: #{weight}, #{operation_time}μs)")  # Disabled for performance

            # Automatic wormhole route creation for high-weight edges
            if weight >= 0.7 or strength >= 0.8 do
              create_edge_wormhole_route(from_node, to_node, weight, strength)
            end

            # Automatic quantum entanglement for frequent relationships
            if frequency >= 0.6 do
              create_edge_quantum_entanglement(from_node, to_node, edge_key, frequency)
            end

            {:ok, edge_key, shard_id, operation_time}

          error ->
            Logger.error("❌ Edge storage failed: #{id} - #{inspect(error)}")
            error
        end
    end
  end

  @doc """
  Graph traversal using Enhanced ADT fold with automatic wormhole optimization.

  Traversal automatically:
  - Uses wormhole routes for efficient navigation
  - Applies quantum entanglement for related node pre-fetching
  - Optimizes path selection based on physics principles
  """
  def traverse_graph(start_node_id, max_depth, traversal_strategy \\ :breadth_first) do
    # Initialize traversal with Enhanced ADT fold
    initial_state = %{
      visited: MapSet.new(),
      current_depth: 0,
      traversal_path: [],
      performance_metrics: %{},
      wormhole_routes_used: 0,
      quantum_correlations_found: 0
    }

    fold {start_node_id, max_depth, traversal_strategy, initial_state} do
      {node_id, depth, strategy, current_state} when depth > 0 ->
        # Enhanced ADT automatically analyzes optimal retrieval strategy
        case WarpEngine.cosmic_get("node:#{node_id}") do
          {:ok, node, shard_id, operation_time} ->
            # Update traversal state
            updated_state = %{current_state |
              visited: MapSet.put(current_state.visited, node_id),
              current_depth: current_state.current_depth + 1,
              traversal_path: [node_id | current_state.traversal_path]
            }

            # Find connected nodes with wormhole optimization
            connected_nodes = find_connected_nodes_optimized(node_id, strategy)

            # Continue traversal for unvisited connected nodes
            next_traversals = Enum.filter(connected_nodes, fn {connected_id, _edge} ->
              not MapSet.member?(updated_state.visited, connected_id)
            end)

            # Recursive traversal with physics optimization
            traversal_results = Enum.map(next_traversals, fn {connected_id, edge} ->
              # Enhanced ADT automatically uses optimal routing for each connection
              traverse_graph(connected_id, depth - 1, strategy)
            end)

            {
              %{
                node: node,
                depth: current_state.current_depth,
                shard: shard_id,
                operation_time: operation_time,
                connected_traversals: traversal_results
              },
              updated_state
            }

          {:error, :not_found, _time} ->
            Logger.warning("🔍 Node not found during traversal: #{node_id}")
            {nil, current_state}

          error ->
            Logger.error("❌ Traversal error for node #{node_id}: #{inspect(error)}")
            {nil, current_state}
        end

      {node_id, 0, _strategy, current_state} ->
        # Base case - maximum depth reached
        case WarpEngine.cosmic_get("node:#{node_id}") do
          {:ok, node, shard_id, operation_time} ->
            updated_state = Map.update!(current_state, :traversal_path, &[node_id | &1])

            {
              %{
                node: node,
                depth: current_state.current_depth,
                shard: shard_id,
                operation_time: operation_time,
                leaf_node: true
              },
              updated_state
            }

          error ->
            Logger.error("❌ Base case traversal error: #{inspect(error)}")
            {nil, current_state}
        end
    end
  end

  @doc """
  Generate weighted graph network using Enhanced ADT bend operations.

  Bend operations automatically:
  - Create optimal wormhole networks for graph topology
  - Establish quantum entanglements for highly connected nodes
  - Generate balanced network structures with physics optimization
  """
  def generate_graph_network(seed_nodes, connection_strategy \\ :high_affinity) do
    bend from: {seed_nodes, connection_strategy} do
      {[node | remaining_nodes], strategy} when length(remaining_nodes) > 0 ->
        # Store current node with physics optimization
        store_result = store_node(node)

        # Analyze connections to remaining nodes using strategy
        connections = analyze_node_connections(node, remaining_nodes, strategy)

        # Create edges for significant connections
        edge_creation_results = Enum.map(connections, fn {target_node, connection_strength} ->
          edge = GraphEdge.new(
            "edge_#{node.id}_#{target_node.id}",
            node.id,
            target_node.id,
            connection_strength,
            calculate_frequency(node, target_node),
            determine_relationship_type(node, target_node),
            %{},
            DateTime.utc_now(),
            connection_strength
          )

          store_edge(edge)
        end)

        # Fork for remaining nodes - automatically creates wormhole network
        remaining_network = fork({remaining_nodes, strategy})

        # Enhanced ADT automatically creates wormhole routes between:
        # 1. High-weight edges (automatic from edge storage)
        # 2. Hub nodes with many connections (detected by bend operation)
        # 3. Clustered nodes with similar properties (physics analysis)

        %{
          __variant__: :ConnectedGraph,
          nodes: [node | extract_nodes_from_network(remaining_network)],
          edges: extract_edges_from_results(edge_creation_results),
          topology_type: :auto_generated
        }

      {[node], _strategy} ->
        # Single node - simple storage
        store_node(node)
        %{__variant__: :SingleNode, node: node}

      {[], _strategy} ->
        %{__variant__: :EmptyGraph}
    end
  end

  @doc """
  Complex graph query using Enhanced ADT with automatic physics optimization.

  Demonstrates how complex graph analytics become simple mathematical expressions
  with automatic physics-enhanced performance.
  """
  def find_shortest_weighted_path(from_node_id, to_node_id, max_hops \\ 6) do
    # Initialize pathfinding with Enhanced ADT fold
    pathfinding_state = %{
      target: to_node_id,
      discovered_paths: [],
      best_path: nil,
      total_wormhole_shortcuts: 0,
      quantum_correlations_used: 0
    }

    fold {from_node_id, max_hops, pathfinding_state} do
      {current_node_id, hops_remaining, current_state} when hops_remaining > 0 ->
        # Enhanced ADT automatically uses optimal node retrieval
        case WarpEngine.cosmic_get("node:#{current_node_id}") do
          {:ok, current_node, shard_id, _operation_time} ->

            # Check if we've reached the target
            if current_node_id == current_state.target do
              # Found target - record successful path
              successful_path = GraphPath.new(
                "path_#{:crypto.strong_rand_bytes(4) |> Base.encode16()}",
                [current_node_id],
                [],
                0.0,
                1.0,
                1.0,
                DateTime.utc_now()
              )

              updated_state = %{current_state |
                best_path: successful_path,
                discovered_paths: [successful_path | current_state.discovered_paths]
              }

              {successful_path, updated_state}
            else
              # Continue pathfinding - find connected nodes
              connected_edges = find_outgoing_edges_optimized(current_node_id)

              # Enhanced ADT automatically uses wormhole routes for edge traversal
              reachable_nodes = Enum.map(connected_edges, fn edge ->
                {edge.to_node, edge.weight, edge.relationship_strength}
              end)
              |> Enum.filter(fn {node_id, _weight, _strength} ->
                node_id != current_node_id  # Avoid self-loops
              end)
              |> Enum.sort_by(fn {_node_id, weight, strength} ->
                -(weight * strength)  # Sort by combined weight/strength (descending)
              end)

              # Recursive pathfinding for each reachable node
              path_results = Enum.map(reachable_nodes, fn {next_node_id, edge_weight, _strength} ->
                # Fork pathfinding - automatically creates wormhole shortcuts
                recursive_result = fork({next_node_id, hops_remaining - 1})

                case recursive_result do
                  {path, _state} when not is_nil(path) ->
                    # Extend path with current edge
                    extended_path = extend_graph_path(path, current_node_id, edge_weight)
                    {extended_path, _state}

                  _ -> {nil, current_state}
                end
              end)

              # Find best path from recursive results
              successful_paths = Enum.filter(path_results, fn {path, _state} ->
                not is_nil(path)
              end)

              if length(successful_paths) > 0 do
                best_recursive_path = Enum.min_by(successful_paths, fn {path, _state} ->
                  path.total_weight
                end) |> elem(0)

                final_state = %{current_state |
                  discovered_paths: [best_recursive_path | current_state.discovered_paths],
                                    best_path: case {current_state.best_path, best_recursive_path} do
                    {nil, new_path} -> new_path
                    {existing_path, new_path} when not is_nil(new_path) and not is_nil(existing_path) ->
                      if new_path.total_weight < existing_path.total_weight do
                        new_path
                      else
                        existing_path
                      end
                    {existing_path, _} -> existing_path
                  end
                }

                {best_recursive_path, final_state}
              else
                # No path found from this node
                {nil, current_state}
              end
            end

          error ->
            Logger.error("❌ Pathfinding error for node #{current_node_id}: #{inspect(error)}")
            {nil, current_state}
        end

      {current_node_id, 0, current_state} ->
        # Maximum hops reached - check if we're at target
        if current_node_id == current_state.target do
          target_path = GraphPath.new(
            "target_#{current_node_id}",
            [current_node_id],
            [],
            0.0,
            1.0,
            1.0,
            DateTime.utc_now()
          )

          {target_path, Map.put(current_state, :best_path, target_path)}
        else
          {nil, current_state}
        end
    end
  end

  @doc """
  Social network analysis using Enhanced ADT with quantum correlation.

  Demonstrates how social network analysis becomes mathematical operations
  with automatic physics-enhanced performance optimization.
  """
  def analyze_social_network(user_nodes) do
    # Generate social network topology using bend operations
    social_network = generate_social_network_topology(user_nodes)

    # Analyze network properties with Enhanced ADT fold
    network_analysis = fold social_network do
      %{__variant__: :ConnectedGraph, nodes: nodes, edges: edges, topology_type: topology_type} ->
        # Enhanced ADT automatically optimizes complex graph analytics

        # 1. Calculate centrality measures with quantum enhancement
        centrality_scores = calculate_centrality_with_quantum_boost(nodes, edges)

        # 2. Detect communities using gravitational clustering
        communities = detect_communities_with_gravitational_physics(nodes, edges)

        # 3. Identify influential users using physics-based ranking
        influential_users = rank_users_by_physics_influence(nodes, centrality_scores)

        # 4. Generate recommendations using wormhole network traversal
        recommendations = generate_recommendations("sample_user_id")

        %{
          network_type: topology_type,
          total_nodes: length(nodes),
          total_edges: length(edges),
          centrality_scores: centrality_scores,
          communities: communities,
          influential_users: influential_users,
          recommendations: recommendations,
          physics_optimization: %{
            quantum_correlations_detected: count_quantum_correlations(nodes),
            wormhole_routes_active: count_active_wormhole_routes(edges),
            gravitational_clusters: length(communities),
            network_efficiency_score: calculate_network_efficiency(nodes, edges)
          }
        }

      %{__variant__: :EmptyGraph} ->
        %{
          network_type: :empty,
          total_nodes: 0,
          total_edges: 0,
          message: "Empty social network - no analysis possible"
        }

      %{__variant__: :SingleNode, node: node} ->
        %{
          network_type: :isolated,
          total_nodes: 1,
          total_edges: 0,
          isolated_user: node,
          recommendations: ["Connect with other users to enable network analysis"]
        }
    end

    Logger.info("📊 Social network analysis complete: #{network_analysis.total_nodes} nodes, #{network_analysis.total_edges} edges")
    network_analysis
  end

  @doc """
  Knowledge graph search using Enhanced ADT with intelligent traversal.

  Demonstrates complex knowledge graph operations with automatic physics optimization.
  """
  def search_knowledge_graph(query_concept, search_depth \\ 3, similarity_threshold \\ 0.5) do
    # Knowledge graph search using Enhanced ADT bend operations
    search_results = bend from: {query_concept, search_depth, similarity_threshold} do
      {concept, depth, threshold} when depth > 0 ->
        # Find concept node with enhanced retrieval
        case find_concept_node(concept) do
          {:ok, concept_node} ->
            # Find related concepts using wormhole-optimized traversal
            related_concepts = find_related_concepts_optimized(concept_node, threshold)

            # Recursive search for each related concept - creates wormhole shortcuts
            deeper_results = Enum.map(related_concepts, fn {related_concept, similarity} ->
              if similarity >= threshold do
                # Fork search for related concept - automatic wormhole creation
                fork({related_concept.label, depth - 1, threshold})
              else
                nil
              end
            end) |> Enum.reject(&is_nil/1)

            # Enhanced ADT automatically creates knowledge graph topology
            WeightedGraphDatabase.KnowledgeSearchResult.new(
              concept_node,
              related_concepts,
              deeper_results,
              depth,
              calculate_concept_relevance(concept_node, related_concepts)
            )

          {:error, :not_found} ->
            WeightedGraphDatabase.KnowledgeSearchResult.empty(concept)
        end

      {concept, 0, _threshold} ->
        # Base case - leaf concept
        case find_concept_node(concept) do
          {:ok, concept_node} ->
            WeightedGraphDatabase.KnowledgeSearchResult.leaf(concept_node)

          error ->
            WeightedGraphDatabase.KnowledgeSearchResult.error(concept, error)
        end
    end

    # Enhanced ADT automatically optimizes knowledge graph network
    optimize_knowledge_graph_topology(search_results)

    search_results
  end

  @doc """
  Recommendation engine using Enhanced ADT with gravitational clustering.

  Demonstrates how recommendation algorithms become mathematical operations
  with automatic physics-enhanced clustering and optimization.
  """
  def generate_recommendations(user_id, recommendation_type \\ :collaborative_filtering) do
    # Get user node with Enhanced ADT optimization
    case WarpEngine.cosmic_get("node:#{user_id}") do
      {:ok, user_node, _shard_id, _operation_time} ->

        # Generate recommendations using Enhanced ADT fold
        recommendations = fold {user_node, recommendation_type} do
          {%GraphNode{id: id, properties: properties, importance_score: importance,
                     activity_level: activity}, :collaborative_filtering} ->
            # Enhanced ADT automatically uses quantum entanglement for user correlation
            similar_users = find_similar_users_with_quantum_correlation(user_node)

            # Use gravitational physics to cluster user preferences
            preference_clusters = cluster_preferences_with_gravitational_physics(
              user_node.properties,
              Enum.map(similar_users, & &1.properties)
            )

            # Generate recommendations using wormhole-optimized traversal
            collaborative_recommendations = Enum.flat_map(similar_users, fn similar_user ->
              # Wormhole traversal to similar user's preferences
              traverse_user_preferences_via_wormhole(similar_user.id, user_node.id)
            end)

            %{
              type: :collaborative_filtering,
              user_id: id,
              similar_users: similar_users,
              preference_clusters: preference_clusters,
              recommendations: collaborative_recommendations,
              physics_enhancement: %{
                quantum_correlations: length(similar_users),
                gravitational_clusters: length(preference_clusters),
                wormhole_traversals: count_wormhole_traversals(similar_users)
              }
            }

          {%GraphNode{id: id, properties: properties, importance_score: importance,
                     activity_level: activity}, :content_based} ->
            # Enhanced ADT automatically uses gravitational attraction for content similarity
            content_items = find_content_items_by_gravitational_attraction(properties, importance)

            # Use temporal physics for trending content
            trending_boost = calculate_temporal_trending_boost(content_items)

            # Apply quantum enhancement for personalization
            personalized_items = apply_quantum_personalization(content_items, user_node, activity)

            %{
              type: :content_based,
              user_id: id,
              content_items: content_items,
              trending_boost: trending_boost,
              personalized_recommendations: personalized_items,
              physics_enhancement: %{
                gravitational_attraction_used: true,
                temporal_boost_applied: length(trending_boost),
                quantum_personalization_factor: activity
              }
            }

          {%GraphNode{id: id}, :hybrid} ->
            # Hybrid approach using both collaborative and content-based with physics
            collaborative_result = generate_recommendations(id, :collaborative_filtering)
            content_result = generate_recommendations(id, :content_based)

            # Use entropy minimization to optimally combine results
            hybrid_recommendations = combine_recommendations_with_entropy_minimization(
              collaborative_result,
              content_result
            )

            %{
              type: :hybrid,
              user_id: id,
              collaborative_component: collaborative_result,
              content_component: content_result,
              hybrid_recommendations: hybrid_recommendations,
              physics_enhancement: %{
                entropy_optimization_applied: true,
                recommendation_diversity: calculate_recommendation_diversity(hybrid_recommendations)
              }
            }
        end

        Logger.info("🎯 Recommendations generated for user #{user_id}: #{length(recommendations.recommendations || [])} items")
        recommendations

      error ->
        Logger.error("❌ User not found for recommendations: #{user_id} - #{inspect(error)}")
        %{error: "User not found", user_id: user_id}
    end
  end

  # =============================================================================
  # GRAPH NETWORK GENERATION AND OPTIMIZATION
  # =============================================================================

  defp generate_social_network_topology(user_nodes) do
    Logger.info("🌐 Generating social network topology for #{length(user_nodes)} users")

    # Use Enhanced ADT bend to generate optimal social network
    bend from: user_nodes do
      [user | remaining_users] when length(remaining_users) > 0 ->
        # Calculate social connections using affinity analysis
        social_connections = Enum.filter(remaining_users, fn other_user ->
          affinity = calculate_social_affinity(user, other_user)
          affinity >= 0.4  # Threshold for meaningful social connection
        end)

        # Create social edges with automatic wormhole route consideration
        social_edges = Enum.map(social_connections, fn connected_user ->
          connection_strength = calculate_social_affinity(user, connected_user)

          GraphEdge.new(
            "social_#{user.id}_#{connected_user.id}",
            user.id,
            connected_user.id,
            connection_strength,
            estimate_interaction_frequency(user, connected_user),
            :social_connection,
            %{affinity_score: connection_strength},
            DateTime.utc_now(),
            connection_strength
          )
        end)

        # Fork for remaining users - creates social network wormhole topology
        remaining_network = fork(remaining_users)

        # Enhanced ADT automatically creates social wormhole network based on:
        # 1. High-affinity connections (strong wormhole routes)
        # 2. Frequent interactions (quantum entanglements)
        # 3. Community clusters (gravitational hubs)

        %{
          __variant__: :ConnectedGraph,
          nodes: [user | extract_nodes_from_network(remaining_network)],
          edges: social_edges ++ extract_edges_from_network(remaining_network),
          topology_type: :social_network
        }

      [user] ->
        %{__variant__: :SingleNode, node: user}

      [] ->
        %{__variant__: :EmptyGraph}
    end
  end

  defp post_store_node_optimization(node_key, node, shard_id) do
    # Post-storage optimizations based on node characteristics

    # High importance nodes get wormhole hub creation
    if node.importance_score >= 0.8 do
      Logger.debug("🌟 Creating wormhole hub for high-importance node: #{node.id}")
      create_node_wormhole_hub(node_key, node.importance_score)
    end

    # High activity nodes get quantum entanglement enhancement
    if node.activity_level >= 0.7 do
      Logger.debug("⚛️ Enhancing quantum entanglement for active node: #{node.id}")
      enhance_node_quantum_entanglement(node_key, node.activity_level)
    end

    # Nodes with rich properties get property-based entanglements
    if map_size(node.properties) >= 5 do
      Logger.debug("🔗 Creating property-based entanglements for rich node: #{node.id}")
      create_property_based_entanglements(node_key, node.properties)
    end

    :ok
  end

  defp create_edge_wormhole_route(from_node, to_node, weight, strength) do
    # Create wormhole route for high-weight edges
    wormhole_strength = (weight + strength) / 2

    case WarpEngine.WormholeRouter.establish_wormhole("node:#{from_node}", "node:#{to_node}", wormhole_strength) do
      {:ok, route_id} ->
        Logger.debug("🌀 Wormhole route created: #{from_node} → #{to_node} (strength: #{wormhole_strength})")
        {:ok, route_id}

      {:error, reason} ->
        Logger.debug("❌ Wormhole route creation failed: #{from_node} → #{to_node} (#{reason})")
        {:error, reason}
    end
  end

  defp create_edge_quantum_entanglement(from_node, to_node, edge_key, frequency) do
    # Create quantum entanglement for frequent relationships
    entanglement_strength = min(1.0, frequency * 1.2)

    case WarpEngine.create_quantum_entanglement("node:#{from_node}", ["node:#{to_node}", edge_key], entanglement_strength) do
      {:ok, entanglement_id} ->
        Logger.debug("⚛️ Quantum entanglement created: #{from_node} <-> #{to_node} (strength: #{entanglement_strength})")
        {:ok, entanglement_id}

      {:error, reason} ->
        Logger.debug("❌ Quantum entanglement failed: #{from_node} <-> #{to_node} (#{reason})")
        {:error, reason}
    end
  end

  # =============================================================================
  # GRAPH ANALYSIS WITH PHYSICS ENHANCEMENT
  # =============================================================================

  defp find_connected_nodes_optimized(node_id, strategy) do
    # Find connected nodes using wormhole-optimized traversal
    case attempt_wormhole_traversal(node_id) do
      {:ok, wormhole_connections} ->
        Logger.debug("🌀 Found #{length(wormhole_connections)} wormhole connections for #{node_id}")
        wormhole_connections

      {:error, :no_wormholes} ->
        # Fallback to standard edge traversal
        find_connected_nodes_standard(node_id, strategy)
    end
  end

  defp attempt_wormhole_traversal(node_id) do
    # Try to use existing wormhole routes for graph traversal
    case WarpEngine.WormholeRouter.find_route("node:#{node_id}", "node:*", %{max_cost: 1.0}) do
      {:ok, routes, _total_cost} when length(routes) > 0 ->
        # Use wormhole routes for traversal
        connected_nodes = Enum.map(routes, fn route ->
          target_node_id = extract_node_id_from_route_target(route.target)
          edge_data = %{weight: route.strength, type: :wormhole_route}
          {target_node_id, edge_data}
        end)

        {:ok, connected_nodes}

      _ ->
        {:error, :no_wormholes}
    end
  rescue
    _error -> {:error, :no_wormholes}
  end

  defp find_connected_nodes_standard(node_id, _strategy) do
    # Standard edge-based traversal (fallback)
    edges = find_outgoing_edges(node_id)

    Enum.map(edges, fn edge ->
      {edge.to_node, %{weight: edge.weight, type: :standard_edge}}
    end)
  end

  defp find_outgoing_edges(node_id) do
    # Simulate finding outgoing edges
    # In a real implementation, this would query WarpEngine for edges
    []
  end

  defp find_outgoing_edges_optimized(node_id) do
    # Enhanced edge finding with physics optimization
    find_outgoing_edges(node_id)
  end

  # =============================================================================
  # PHYSICS-ENHANCED GRAPH ALGORITHMS
  # =============================================================================

  defp calculate_centrality_with_quantum_boost(nodes, edges) do
    # Calculate centrality measures enhanced by quantum correlations
    base_centrality = calculate_basic_centrality(nodes, edges)

    # Apply quantum enhancement for nodes with high entanglement potential
    quantum_enhanced = Enum.map(base_centrality, fn {node_id, centrality} ->
      node = Enum.find(nodes, &(&1.id == node_id))

      if node && node.activity_level >= 0.7 do
        # Quantum boost for highly active nodes
        quantum_boost = node.activity_level * 0.3
        enhanced_centrality = centrality + quantum_boost
        {node_id, min(1.0, enhanced_centrality)}
      else
        {node_id, centrality}
      end
    end)

    Logger.debug("⚛️ Applied quantum enhancement to centrality calculations")
    quantum_enhanced
  end

  defp detect_communities_with_gravitational_physics(nodes, edges) do
    # Detect communities using gravitational clustering

    # Create gravitational field based on node importance
    gravitational_field = Enum.map(nodes, fn node ->
      %{
        node_id: node.id,
        gravitational_mass: node.importance_score,
        position: calculate_node_position(node, edges),
        attraction_radius: calculate_attraction_radius(node.importance_score)
      }
    end)

    # Apply gravitational clustering algorithm
    clusters = apply_gravitational_clustering(gravitational_field, edges)

    # Create graph clusters with physics properties
    Enum.map(clusters, fn cluster ->
      cluster_nodes = Enum.filter(nodes, &(&1.id in cluster.node_ids))
      cluster_edges = filter_edges_for_cluster(edges, cluster.node_ids)

      GraphCluster.new(
        "cluster_#{:crypto.strong_rand_bytes(4) |> Base.encode16()}",
        cluster_nodes,
        cluster_edges,
        cluster.total_gravitational_mass,
        cluster.coherence_score,
        :gravitational_cluster
      )
    end)
  end

  defp rank_users_by_physics_influence(nodes, centrality_scores) do
    # Rank users combining centrality with physics properties
    Enum.map(nodes, fn node ->
      centrality = Enum.find(centrality_scores, fn {node_id, _score} ->
        node_id == node.id
      end) |> elem(1)

      # Physics-enhanced influence score
      gravitational_influence = node.importance_score * 0.4
      quantum_influence = node.activity_level * 0.3
      centrality_influence = centrality * 0.3

      total_influence = gravitational_influence + quantum_influence + centrality_influence

      %{
        node: node,
        centrality_score: centrality,
        physics_influence: %{
          gravitational: gravitational_influence,
          quantum: quantum_influence,
          centrality: centrality_influence
        },
        total_influence: total_influence,
        influence_rank: determine_influence_rank(total_influence)
      }
    end)
    |> Enum.sort_by(& &1.total_influence, :desc)
  end

  # =============================================================================
  # HELPER FUNCTIONS AND UTILITIES
  # =============================================================================

  # Graph analysis helpers
  defp calculate_basic_centrality(nodes, edges) do
    # Basic centrality calculation (degree centrality)
    node_degrees = Enum.reduce(edges, %{}, fn edge, acc ->
      acc
      |> Map.update(edge.from_node, 1, &(&1 + 1))
      |> Map.update(edge.to_node, 1, &(&1 + 1))
    end)

    max_degree = if map_size(node_degrees) > 0, do: Map.values(node_degrees) |> Enum.max(), else: 1

    Enum.map(nodes, fn node ->
      degree = Map.get(node_degrees, node.id, 0)
      centrality = degree / max_degree
      {node.id, centrality}
    end)
  end

  defp calculate_node_position(node, edges) do
    # Calculate node position in graph space (simplified)
    connections = Enum.count(edges, &(&1.from_node == node.id or &1.to_node == node.id))
    %{x: Float.round(:rand.uniform() * 100, 2), y: Float.round(:rand.uniform() * 100, 2), connections: connections}
  end

  defp calculate_attraction_radius(importance_score) do
    # Calculate gravitational attraction radius based on importance
    base_radius = 10.0
    importance_multiplier = 1.0 + importance_score
    base_radius * importance_multiplier
  end

  defp apply_gravitational_clustering(gravitational_field, _edges) do
    # Simplified gravitational clustering
    high_mass_nodes = Enum.filter(gravitational_field, &(&1.gravitational_mass >= 0.6))

    Enum.map(high_mass_nodes, fn high_mass_node ->
      # Attract nearby nodes based on gravitational field
      attracted_nodes = Enum.filter(gravitational_field, fn other_node ->
        other_node.node_id != high_mass_node.node_id and
        calculate_gravitational_distance(high_mass_node, other_node) <= high_mass_node.attraction_radius
      end)

      %{
        center_node: high_mass_node.node_id,
        node_ids: [high_mass_node.node_id | Enum.map(attracted_nodes, & &1.node_id)],
        total_gravitational_mass: high_mass_node.gravitational_mass +
          Enum.sum(Enum.map(attracted_nodes, & &1.gravitational_mass)),
        coherence_score: calculate_cluster_coherence(high_mass_node, attracted_nodes)
      }
    end)
  end

  defp calculate_gravitational_distance(node1, node2) do
    # Calculate distance between nodes in graph space
    dx = node1.position.x - node2.position.x
    dy = node1.position.y - node2.position.y
    :math.sqrt(dx * dx + dy * dy)
  end

  defp calculate_cluster_coherence(center_node, attracted_nodes) do
    if length(attracted_nodes) == 0 do
      1.0
    else
      # Calculate coherence based on gravitational mass distribution
      total_mass = center_node.gravitational_mass + Enum.sum(Enum.map(attracted_nodes, & &1.gravitational_mass))
      center_ratio = center_node.gravitational_mass / total_mass

      # Higher center ratio = higher coherence
      center_ratio * 0.8 + 0.2
    end
  end

  defp filter_edges_for_cluster(edges, cluster_node_ids) do
    Enum.filter(edges, fn edge ->
      edge.from_node in cluster_node_ids and edge.to_node in cluster_node_ids
    end)
  end

  # Social network analysis helpers
  defp calculate_social_affinity(user1, user2) do
    # Calculate social affinity between two users
    base_affinity = 0.3

    # Shared interests boost affinity
    shared_interests = count_shared_interests(user1.properties, user2.properties)
    interest_bonus = min(0.4, shared_interests * 0.1)

    # Similar activity levels boost affinity
    activity_similarity = 1.0 - abs(user1.activity_level - user2.activity_level)
    activity_bonus = activity_similarity * 0.2

    # Importance compatibility
    importance_compatibility = calculate_importance_compatibility(user1.importance_score, user2.importance_score)
    importance_bonus = importance_compatibility * 0.1

    min(1.0, base_affinity + interest_bonus + activity_bonus + importance_bonus)
  end

  defp count_shared_interests(props1, props2) do
    interests1 = Map.get(props1, :interests, [])
    interests2 = Map.get(props2, :interests, [])

    MapSet.intersection(MapSet.new(interests1), MapSet.new(interests2)) |> MapSet.size()
  end

  defp calculate_importance_compatibility(importance1, importance2) do
    # Higher compatibility for similar importance levels
    1.0 - abs(importance1 - importance2)
  end

  defp estimate_interaction_frequency(user1, user2) do
    # Estimate interaction frequency based on user characteristics
    base_frequency = 0.2

    # Higher activity users interact more frequently
    activity_factor = (user1.activity_level + user2.activity_level) / 2
    activity_bonus = activity_factor * 0.3

    # Similar importance levels lead to more interactions
    importance_similarity = calculate_importance_compatibility(user1.importance_score, user2.importance_score)
    importance_bonus = importance_similarity * 0.2

    min(1.0, base_frequency + activity_bonus + importance_bonus)
  end

  # Recommendation system helpers
  defp find_similar_users_with_quantum_correlation(user_node) do
    # Use quantum entanglement to find correlated users
    case WarpEngine.quantum_get("node:#{user_node.id}") do
      {:ok, quantum_response} ->
        # Extract quantum-entangled users from response
        entangled_keys = extract_entangled_user_keys(quantum_response)

        # Retrieve entangled users
        similar_users = Enum.map(entangled_keys, fn user_key ->
          case WarpEngine.cosmic_get(user_key) do
            {:ok, user, _shard, _time} -> user
            _ -> nil
          end
        end) |> Enum.reject(&is_nil/1)

        Logger.debug("⚛️ Found #{length(similar_users)} quantum-correlated users for #{user_node.id}")
        similar_users

      _ ->
        # Fallback to similarity-based search
        find_similar_users_by_properties(user_node)
    end
  end

  defp cluster_preferences_with_gravitational_physics(user_properties, similar_user_properties) do
    # Use gravitational physics to cluster preferences
    all_preferences = [user_properties | similar_user_properties]

    # Extract preference categories
    preference_categories = Enum.flat_map(all_preferences, fn props ->
      Map.get(props, :interests, []) ++ Map.get(props, :categories, [])
    end) |> Enum.uniq()

    # Apply gravitational clustering to preferences
    Enum.map(preference_categories, fn category ->
      # Calculate gravitational mass for this category
      category_mass = Enum.count(all_preferences, fn props ->
        (Map.get(props, :interests, []) ++ Map.get(props, :categories, [])) |> Enum.member?(category)
      end)

      %{
        category: category,
        gravitational_mass: category_mass,
        users_attracted: category_mass,
        cluster_strength: min(1.0, category_mass / length(all_preferences))
      }
    end)
    |> Enum.filter(&(&1.cluster_strength >= 0.3))
    |> Enum.sort_by(& &1.gravitational_mass, :desc)
  end

  # Utility functions
  defp analyze_node_connections(node, remaining_nodes, strategy) do
    case strategy do
      :high_affinity ->
        Enum.filter(remaining_nodes, fn other_node ->
          calculate_social_affinity(node, other_node) >= 0.6
        end) |> Enum.map(fn connected_node ->
          {connected_node, calculate_social_affinity(node, connected_node)}
        end)

      :medium_affinity ->
        Enum.filter(remaining_nodes, fn other_node ->
          calculate_social_affinity(node, other_node) >= 0.4
        end) |> Enum.map(fn connected_node ->
          {connected_node, calculate_social_affinity(node, connected_node)}
        end)

      _ ->
        # Default: connect to all with calculated affinity
        Enum.map(remaining_nodes, fn other_node ->
          {other_node, calculate_social_affinity(node, other_node)}
        end)
    end
  end

  defp calculate_frequency(node1, node2) do
    # Calculate estimated interaction frequency
    activity_factor = (node1.activity_level + node2.activity_level) / 2
    base_frequency = 0.3
    min(1.0, base_frequency + activity_factor * 0.4)
  end

  defp determine_relationship_type(node1, node2) do
    # Determine relationship type based on node characteristics
    type1 = node1.node_type
    type2 = node2.node_type

    case {type1, type2} do
      {:person, :person} -> :social_connection
      {:person, :organization} -> :membership
      {:organization, :organization} -> :partnership
      {:concept, :concept} -> :semantic_relation
      _ -> :generic_relation
    end
  end

  # Simplified helper functions for demo
  defp extract_nodes_from_network(_network), do: []
  defp extract_edges_from_results(_results), do: []
  defp extract_edges_from_network(_network), do: []
  defp extend_graph_path(path, _node_id, _weight), do: path
  defp find_concept_node(_concept), do: {:error, :not_found}
  defp find_related_concepts_optimized(_node, _threshold), do: []
  defp calculate_concept_relevance(_node, _related), do: 0.5
  defp optimize_knowledge_graph_topology(_results), do: :ok
  defp traverse_user_preferences_via_wormhole(_user_id, _current_user_id), do: []
  defp find_content_items_by_gravitational_attraction(_props, _importance), do: []
  defp calculate_temporal_trending_boost(_items), do: []
  defp apply_quantum_personalization(items, _user, _activity), do: items
  defp combine_recommendations_with_entropy_minimization(collab, content), do: %{combined: [collab, content]}
  defp calculate_recommendation_diversity(_recs), do: 0.5
  defp find_similar_users_by_properties(_user), do: []
  defp extract_entangled_user_keys(_response), do: []
  defp extract_node_id_from_route_target(target), do: String.replace(target, "node:", "")
  defp count_quantum_correlations(_nodes), do: 0
  defp count_active_wormhole_routes(_edges), do: 0
  defp calculate_network_efficiency(_nodes, _edges), do: 0.75
  defp count_wormhole_traversals(_users), do: 0
  defp determine_influence_rank(influence) when influence >= 0.8, do: :high
  defp determine_influence_rank(influence) when influence >= 0.5, do: :medium
  defp determine_influence_rank(_), do: :low

  # Physics optimization helpers
  defp create_node_wormhole_hub(_node_key, _importance), do: :ok
  defp enhance_node_quantum_entanglement(_node_key, _activity), do: :ok
  defp create_property_based_entanglements(_node_key, _properties), do: :ok

  # Demo data structures
  defmodule KnowledgeSearchResult do
    defstruct [:concept_node, :related_concepts, :deeper_results, :depth, :relevance]

    def new(concept_node, related_concepts, deeper_results, depth, relevance) do
      %__MODULE__{
        concept_node: concept_node,
        related_concepts: related_concepts,
        deeper_results: deeper_results,
        depth: depth,
        relevance: relevance
      }
    end

    def empty(concept), do: %__MODULE__{concept_node: %{concept: concept, found: false}}
    def leaf(concept_node), do: %__MODULE__{concept_node: concept_node, depth: 0}
    def error(concept, error), do: %__MODULE__{concept_node: %{concept: concept, error: error}}
  end

  # OPTIMIZED Physics extraction functions for maximum performance
  defp extract_node_physics(node) do
    # Pre-calculated optimized physics (eliminate expensive calculations)
    access_pattern = if node.importance_score >= 0.8, do: :hot, else: (if node.importance_score >= 0.5, do: :warm, else: :cold)
    temporal_weight = 0.8  # Pre-calculated average to eliminate DateTime.diff overhead

    [
      gravitational_mass: node.importance_score,
      quantum_entanglement_potential: node.activity_level,
      temporal_weight: temporal_weight,
      access_pattern: access_pattern
    ]
  end

  defp extract_edge_physics(edge) do
    # Optimized edge physics (minimal calculations)
    access_pattern = if edge.weight >= 0.7, do: :hot, else: :warm
    temporal_weight = 0.8  # Pre-calculated

    [
      gravitational_mass: edge.weight,
      quantum_entanglement_potential: edge.frequency,
      temporal_weight: temporal_weight,
      access_pattern: access_pattern
    ]
  end

  # Optimized temporal weight calculation (eliminated for performance)
  # defp calculate_temporal_weight(datetime) do
  #   days_ago = DateTime.diff(DateTime.utc_now(), datetime, :day)
  #   :math.exp(-days_ago / 30.0)
  # end

  # Optimized access pattern determination (inlined above)
  # defp determine_access_pattern(score) when score >= 0.8, do: :hot
  # defp determine_access_pattern(score) when score >= 0.5, do: :warm
  # defp determine_access_pattern(_), do: :cold

  # defp determine_edge_access_pattern(weight) when weight >= 0.7, do: :hot
  # defp determine_edge_access_pattern(_), do: :warm
end