Category: Analytics And Ml

Graph analytics and machine learning represent one of the most powerful applications of graph database technology. Geode provides a comprehensive platform for advanced analytics, combining native graph algorithms, vector embeddings, full-text search, and seamless integration with modern ML frameworks. This enables organizations to extract insights from connected data, build recommendation systems, detect anomalies, identify communities, and power intelligent applications. Geode’s analytics capabilities leverage its graph structure to efficiently compute metrics that would require complex joins in relational databases. Built-in algorithms for centrality, community detection, pathfinding, and similarity analysis run directly on Geode’s storage engine with optimizations for graph traversal. Vector search with HNSW indexing enables semantic similarity queries for AI/ML workloads, while BM25 full-text search powers content discovery and ranking. The platform’s ISO/IEC 39075:2024 compliance ensures that analytics queries use standard syntax, while ACID transactions guarantee data consistency even when updating analytical models. This category explores how to leverage Geode for graph analytics, integrate with ML pipelines, and build intelligent data-driven applications. <h3 id="graph-analytics-fundamentals" class="position-relative d-flex align-items-center group"> Graph Analytics Fundamentals <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="graph-analytics-fundamentals" aria-haspopup="dialog" aria-label="Share link: Graph Analytics Fundamentals"> Share link </button> </h3><div id="headingShareModal" class="heading-share-modal" role="dialog" aria-modal="true" aria-labelledby="headingShareTitle" hidden> <div class="hsm-dialog" role="document"> <div class="hsm-header"> <h2 id="headingShareTitle" class="h6 mb-0 fw-bold">Share this section</h2> <button type="button" class="hsm-close" aria-label="Close"> </button> </div> <div class="hsm-body"> <label for="headingShareInput" class="form-label small text-muted mb-1 text-uppercase fw-bold" style="font-size: 0.7rem; 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if(!icon) return; const prev=copyBtn.getAttribute('data-prev')||icon.className; if(!copyBtn.getAttribute('data-prev')) copyBtn.setAttribute('data-prev',prev); icon.className= ok ? 'fa-duotone fa-clipboard-check':'fa-duotone fa-circle-exclamation'; setTimeout(()=>{ icon.className=prev; },1800); } function handleShareClick(e){ e.preventDefault(); const btn=e.currentTarget; const id=btn.getAttribute('data-share-target'); if(id) openModal(id); } function bindShareButtons(){ document.querySelectorAll('.h-share').forEach(btn=>{ if(!btn.dataset.hShareBound){ btn.addEventListener('click', handleShareClick); btn.dataset.hShareBound='1'; } }); } bindShareButtons(); if(document.readyState==='loading'){ document.addEventListener('DOMContentLoaded', bindShareButtons); } else { requestAnimationFrame(bindShareButtons); } document.addEventListener('click', function(e){ const shareBtn=e.target.closest && e.target.closest('.h-share'); if(shareBtn && !shareBtn.dataset.hShareBound){ handleShareClick.call(shareBtn, e); } }, true); document.addEventListener('click', e=>{ if(e.target===modal) closeModal(); if(e.target.closest && e.target.closest('.hsm-close')){ e.preventDefault(); closeModal(); } if(copyBtn && (e.target===copyBtn || (e.target.closest && e.target.closest('.hsm-copy')))) { e.preventDefault(); copyCurrent(); } }); document.addEventListener('keydown', e=>{ if(e.key==='Escape' && isOpen()) closeModal(); }); function trapFocus(){ if(trapBound) return; trapBound=true; modal.addEventListener('keydown', f=>{ if(f.key==='Tab' && isOpen()){ const focusable=[...modal.querySelectorAll('a[href],button,input,textarea,select,[tabindex]:not([tabindex="-1"])')].filter(el=>!el.hasAttribute('disabled')); if(!focusable.length) return; const first=focusable[0]; const last=focusable[focusable.length-1]; if(f.shiftKey && document.activeElement===first){ f.preventDefault(); last.focus(); } else if(!f.shiftKey && document.activeElement===last){ f.preventDefault(); first.focus(); } } }); } if(closeBtn) closeBtn.addEventListener('click', e=>{ e.preventDefault(); closeModal(); }); })(); </script> <h4 id="understanding-graph-metrics" class="position-relative d-flex align-items-center group"> Understanding Graph Metrics <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="understanding-graph-metrics" aria-haspopup="dialog" aria-label="Share link: Understanding Graph Metrics"> Share link </button> </h4>Graph analytics operate on the relationships between entities, revealing patterns invisible to traditional analytics: <ul> <li>Centrality measures identify influential nodes (PageRank, betweenness, closeness)</li> <li>Community detection reveals natural groupings and clusters</li> <li>Path analysis finds optimal routes and connection patterns</li> <li>Similarity metrics identify related entities based on neighborhood structure</li> <li>Degree distributions characterize network topology</li> </ul> Unlike table scans or index lookups, graph algorithms traverse relationships directly, often providing O(E) complexity where E is the number of edges in the subgraph of interest. <h4 id="native-graph-algorithm-support" class="position-relative d-flex align-items-center group"> Native Graph Algorithm Support <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="native-graph-algorithm-support" aria-haspopup="dialog" aria-label="Share link: Native Graph Algorithm Support"> Share link </button> </h4>Geode implements graph algorithms as native operations optimized for its storage engine: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// PageRank for influence analysis MATCH (n:WebPage) WITH graph.algorithms.pagerank(n, { iterations: 20, dampingFactor: 0.85, tolerance: 0.0001 }) AS rank RETURN n.url, rank ORDER BY rank DESC LIMIT 100 // Community detection with Louvain algorithm CALL graph.algorithms.louvain('social_network', { relationshipTypes: ['FRIEND', 'COLLEAGUE'], includeIntermediateCommunities: true }) YIELD nodeId, communityId, modularity RETURN communityId, COUNT(*) AS members, AVG(modularity) AS cohesion ORDER BY members DESC // Betweenness centrality for bridge detection MATCH (n:Person) WITH graph.algorithms.betweenness_centrality(n) AS centrality WHERE centrality > 100 RETURN n.name, centrality ORDER BY centrality DESC </code></pre></div>These algorithms run in-process without data export, maintaining ACID guarantees and security policies. <h3 id="machine-learning-integration" class="position-relative d-flex align-items-center group"> Machine Learning Integration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="machine-learning-integration" aria-haspopup="dialog" aria-label="Share link: Machine Learning Integration"> Share link </button> </h3> <h4 id="vector-embeddings-and-semantic-search" class="position-relative d-flex align-items-center group"> Vector Embeddings and Semantic Search <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="vector-embeddings-and-semantic-search" aria-haspopup="dialog" aria-label="Share link: Vector Embeddings and Semantic Search"> Share link </button> </h4>Geode’s HNSW (Hierarchical Navigable Small World) index enables approximate nearest neighbor search for vector embeddings, supporting ML workloads: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">from geode_client import Client import numpy as np client = Client(host="localhost", port=3141) async with client.connection() as conn: # Store embeddings from your ML model embedding = model.encode("Graph databases for analytics") await conn.execute(""" CREATE (:Article { title: $title, content: $content, embedding: $embedding }) """, { 'title': 'Graph Analytics Guide', 'content': 'Full article text...', 'embedding': embedding.tolist() # 384-dim vector }) # Semantic similarity search query_embedding = model.encode("machine learning with graphs") results = await conn.execute(""" MATCH (a:Article) WITH a, vector_similarity(a.embedding, $query_vector) AS similarity WHERE similarity > 0.75 RETURN a.title, a.content, similarity ORDER BY similarity DESC LIMIT 10 """, {'query_vector': query_embedding.tolist()}) </code></pre></div>Vector search enables: <ul> <li>Semantic search: Find conceptually similar content</li> <li>Recommendations: Suggest items based on embedding similarity</li> <li>Anomaly detection: Identify outliers in vector space</li> <li>Clustering: Group similar entities using vector distance</li> </ul> <h4 id="embedding-generation-patterns" class="position-relative d-flex align-items-center group"> Embedding Generation Patterns <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="embedding-generation-patterns" aria-haspopup="dialog" aria-label="Share link: Embedding Generation Patterns"> Share link </button> </h4>Integrate with popular embedding models: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Using sentence-transformers from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') async def store_with_embeddings(client, documents): for doc in documents: embedding = model.encode(doc['text']) await client.execute(""" CREATE (:Document { id: $id, text: $text, embedding: $embedding, created_at: datetime() }) """, { 'id': doc['id'], 'text': doc['text'], 'embedding': embedding.tolist() }) # Using OpenAI embeddings import openai async def create_with_openai_embeddings(client, text): response = openai.Embedding.create( model="text-embedding-ada-002", input=text ) embedding = response['data'][0]['embedding'] await client.execute(""" CREATE (:Content { text: $text, embedding: $embedding }) """, {'text': text, 'embedding': embedding}) </code></pre></div> <h4 id="hybrid-search-combining-text-and-semantic" class="position-relative d-flex align-items-center group"> Hybrid Search: Combining Text and Semantic <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="hybrid-search-combining-text-and-semantic" aria-haspopup="dialog" aria-label="Share link: Hybrid Search: Combining Text and Semantic"> Share link </button> </h4>Combine BM25 full-text search with vector similarity for powerful hybrid search: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Hybrid search with weighted score fusion MATCH (doc:Document) WHERE text_search(doc.content, $keywords) WITH doc, bm25_score(doc.content, $keywords) AS text_score, vector_similarity(doc.embedding, $query_vector) AS semantic_score WITH doc, text_score, semantic_score, (0.6 * text_score + 0.4 * semantic_score) AS combined_score WHERE combined_score > 0.5 RETURN doc.title, doc.summary, combined_score ORDER BY combined_score DESC LIMIT 20 </code></pre></div>This approach combines keyword matching with semantic understanding, capturing both exact terms and conceptual relevance. <h3 id="recommendation-systems" class="position-relative d-flex align-items-center group"> Recommendation Systems <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="recommendation-systems" aria-haspopup="dialog" aria-label="Share link: Recommendation Systems"> Share link </button> </h3> <h4 id="collaborative-filtering" class="position-relative d-flex align-items-center group"> Collaborative Filtering <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="collaborative-filtering" aria-haspopup="dialog" aria-label="Share link: Collaborative Filtering"> Share link </button> </h4>Graph structure naturally represents user-item interactions for recommendation engines: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Item-based collaborative filtering MATCH (user:User {id: $user_id})-[:PURCHASED]->(p:Product) MATCH (p)<-[:PURCHASED]-(other:User)-[:PURCHASED]->(rec:Product) WHERE NOT (user)-[:PURCHASED]->(rec) WITH rec, COUNT(DISTINCT other) AS overlap, COUNT(DISTINCT p) AS user_products WITH rec, overlap, user_products, (overlap * 1.0 / user_products) AS jaccard_similarity WHERE jaccard_similarity > 0.3 RETURN rec.name, rec.category, jaccard_similarity ORDER BY jaccard_similarity DESC LIMIT 10 // User-based collaborative filtering with weighted similarity MATCH (user:User {id: $user_id})-[r1:RATED]->(p:Product)<-[r2:RATED]-(similar:User) WITH user, similar, COUNT(p) AS common_products, SUM(ABS(r1.rating - r2.rating)) AS rating_diff WITH similar, common_products, common_products / (1.0 + rating_diff) AS similarity_score ORDER BY similarity_score DESC LIMIT 20 MATCH (similar)-[r:RATED]->(rec:Product) WHERE NOT (user)-[:RATED]->(rec) AND r.rating >= 4.0 RETURN rec.name, AVG(r.rating) AS avg_rating, COUNT(*) AS recommendation_strength ORDER BY recommendation_strength DESC, avg_rating DESC LIMIT 10 </code></pre></div> <h4 id="content-based-recommendations" class="position-relative d-flex align-items-center group"> Content-Based Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="content-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Content-Based Recommendations"> Share link </button> </h4>Combine graph relationships with vector similarity: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Content-based recommendations using embeddings and graph features MATCH (user:User {id: $user_id})-[:LIKED]->(item:Item) WITH COLLECT(item.embedding) AS liked_embeddings, COLLECT(item.category) AS preferred_categories // Create user preference vector (centroid) WITH reduce(sum = [0.0] * 384, emb IN liked_embeddings | vector_add(sum, emb)) AS user_vector, preferred_categories MATCH (candidate:Item) WHERE candidate.category IN preferred_categories AND NOT (user)-[:LIKED|DISLIKED]->(candidate) WITH candidate, vector_similarity(user_vector, candidate.embedding) AS content_similarity WHERE content_similarity > 0.7 RETURN candidate.title, candidate.category, content_similarity ORDER BY content_similarity DESC LIMIT 15 </code></pre></div> <h3 id="anomaly-detection" class="position-relative d-flex align-items-center group"> Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Anomaly Detection"> Share link </button> </h3> <h4 id="graph-based-anomaly-detection" class="position-relative d-flex align-items-center group"> Graph-Based Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="graph-based-anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Graph-Based Anomaly Detection"> Share link </button> </h4>Detect unusual patterns using graph structure: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Detect anomalous transaction patterns MATCH (account:Account)-[t:TRANSACTION]->(recipient:Account) WITH account, COUNT(t) AS tx_count, AVG(t.amount) AS avg_amount, STDDEV(t.amount) AS stddev_amount, COLLECT(DISTINCT recipient.country) AS countries WHERE tx_count > 10 WITH account, tx_count, avg_amount, stddev_amount, countries, SIZE(countries) AS country_count // Flag accounts with unusual patterns MATCH (account)-[t:TRANSACTION]->(r:Account) WHERE t.amount > (avg_amount + 3 * stddev_amount) // Outlier detection OR country_count > 10 // Unusual geographic spread OR tx_count > 100 // High transaction volume RETURN account.id, tx_count, country_count, t.amount AS suspicious_amount, avg_amount + 3 * stddev_amount AS threshold, 'outlier_detection' AS reason </code></pre></div> <h4 id="community-based-anomaly-detection" class="position-relative d-flex align-items-center group"> Community-Based Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="community-based-anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Community-Based Anomaly Detection"> Share link </button> </h4>Identify entities that don’t fit their community: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Detect nodes with unusual community membership CALL graph.algorithms.louvain('transaction_network') YIELD nodeId, communityId MATCH (n) WHERE id(n) = nodeId WITH n, communityId, SIZE((n)--()) AS degree, SIZE((n)--(m) WHERE m.communityId = communityId) AS internal_degree WITH n, communityId, internal_degree * 1.0 / degree AS community_affinity WHERE community_affinity < 0.3 // Weak community membership RETURN n.id, communityId, community_affinity ORDER BY community_affinity ASC </code></pre></div> <h3 id="fraud-detection" class="position-relative d-flex align-items-center group"> Fraud Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="fraud-detection" aria-haspopup="dialog" aria-label="Share link: Fraud Detection"> Share link </button> </h3> <h4 id="pattern-matching-for-fraud" class="position-relative d-flex align-items-center group"> Pattern Matching for Fraud <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="pattern-matching-for-fraud" aria-haspopup="dialog" aria-label="Share link: Pattern Matching for Fraud"> Share link </button> </h4>Graph patterns reveal complex fraud schemes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Detect circular payment patterns (potential money laundering) MATCH path = (a:Account)-[:TRANSFER*3..5]->(a) WHERE ALL(r IN relationships(path) WHERE r.amount > 10000) AND reduce(total = 0, r IN relationships(path) | total + r.amount) > 50000 WITH path, nodes(path) AS accounts, reduce(total = 0, r IN relationships(path) | total + r.amount) AS cycle_amount RETURN accounts, cycle_amount, LENGTH(path) AS cycle_length, 'circular_transfer' AS fraud_type // Detect identity fraud through shared attributes MATCH (a1:Account), (a2:Account) WHERE id(a1) < id(a2) AND a1.phone = a2.phone AND a1.address = a2.address AND a1.email <> a2.email WITH a1, a2, SIZE((a1)-[:TRANSACTION]->()) AS a1_tx, SIZE((a2)-[:TRANSACTION]->()) AS a2_tx WHERE a1_tx > 0 AND a2_tx > 0 RETURN a1.id, a2.id, a1.phone AS shared_phone, a1.address AS shared_address, 'identity_fraud_suspect' AS fraud_type </code></pre></div> <h3 id="time-series-analysis-on-graphs" class="position-relative d-flex align-items-center group"> Time-Series Analysis on Graphs <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="time-series-analysis-on-graphs" aria-haspopup="dialog" aria-label="Share link: Time-Series Analysis on Graphs"> Share link </button> </h3> <h4 id="temporal-pattern-analysis" class="position-relative d-flex align-items-center group"> Temporal Pattern Analysis <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="temporal-pattern-analysis" aria-haspopup="dialog" aria-label="Share link: Temporal Pattern Analysis"> Share link </button> </h4>Combine graph structure with temporal queries: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Analyze user behavior over time MATCH (u:User {id: $user_id})-[a:ACTION]->(entity) WHERE a.timestamp >= datetime() - duration('P30D') WITH u, DATE(a.timestamp) AS day, a.action_type AS action, COUNT(*) AS action_count WITH u, day, COLLECT({action: action, count: action_count}) AS daily_actions RETURN day, daily_actions ORDER BY day ASC // Detect trend changes MATCH (product:Product)<-[sale:SOLD]-(order:Order) WHERE sale.timestamp >= datetime() - duration('P90D') WITH product, DATE(sale.timestamp) AS week, COUNT(*) AS weekly_sales ORDER BY product, week WITH product, COLLECT(weekly_sales) AS sales_series WITH product, sales_series, sales_series[-4..] AS recent_sales, sales_series[0..4] AS early_sales WHERE AVG(recent_sales) > 1.5 * AVG(early_sales) RETURN product.name, AVG(early_sales) AS avg_early_sales, AVG(recent_sales) AS avg_recent_sales, (AVG(recent_sales) - AVG(early_sales)) / AVG(early_sales) AS growth_rate ORDER BY growth_rate DESC </code></pre></div> <h3 id="feature-engineering" class="position-relative d-flex align-items-center group"> Feature Engineering <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="feature-engineering" aria-haspopup="dialog" aria-label="Share link: Feature Engineering"> Share link </button> </h3> <h4 id="graph-features-for-ml-models" class="position-relative d-flex align-items-center group"> Graph Features for ML Models <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="graph-features-for-ml-models" aria-haspopup="dialog" aria-label="Share link: Graph Features for ML Models"> Share link </button> </h4>Extract graph-based features for training ML models: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">from geode_client import Client import pandas as pd async def extract_node_features(client, node_label): """Extract graph features for ML training.""" features, _ = await client.query(f""" MATCH (n:{node_label}) WITH n, SIZE((n)--()) AS degree, SIZE((n)-->()) AS out_degree, SIZE((n)<--()) AS in_degree, graph.algorithms.pagerank(n) AS pagerank, graph.algorithms.clustering_coefficient(n) AS clustering, graph.algorithms.closeness_centrality(n) AS closeness RETURN id(n) AS node_id, degree, out_degree, in_degree, pagerank, clustering, closeness """) return pd.DataFrame([dict(r) for r in features]) # Use features in scikit-learn from sklearn.ensemble import RandomForestClassifier async def train_node_classifier(client): # Extract features df = await extract_node_features(client, 'User') # Get labels (assuming they exist) labels, _ = await client.query(""" MATCH (n:User) RETURN id(n) AS node_id, n.is_fraudulent AS label """) label_df = pd.DataFrame([dict(r) for r in labels]) # Merge and train training_data = df.merge(label_df, on='node_id') X = training_data.drop(['node_id', 'label'], axis=1) y = training_data['label'] model = RandomForestClassifier(n_estimators=100) model.fit(X, y) return model </code></pre></div> <h3 id="performance-optimization-for-analytics" class="position-relative d-flex align-items-center group"> Performance Optimization for Analytics <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="performance-optimization-for-analytics" aria-haspopup="dialog" aria-label="Share link: Performance Optimization for Analytics"> Share link </button> </h3> <h4 id="batch-processing" class="position-relative d-flex align-items-center group"> Batch Processing <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="batch-processing" aria-haspopup="dialog" aria-label="Share link: Batch Processing"> Share link </button> </h4>For large-scale analytics, use batch processing: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Process in batches using SKIP and LIMIT WITH 1000 AS batch_size, 0 AS offset MATCH (n:User) SKIP offset LIMIT batch_size WITH n, graph.algorithms.pagerank(n) AS rank SET n.pagerank = rank RETURN COUNT(*) AS processed // Parallel batch processing (multiple sessions) // Session 1: Process users 0-999 // Session 2: Process users 1000-1999 // etc. </code></pre></div> <h4 id="index-optimization" class="position-relative d-flex align-items-center group"> Index Optimization <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="index-optimization" aria-haspopup="dialog" aria-label="Share link: Index Optimization"> Share link </button> </h4>Create indexes for analytical queries: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Create indexes for common analytical patterns CREATE INDEX user_activity_idx ON :User(last_active_date, registration_date) CREATE INDEX transaction_time_idx ON :Transaction(timestamp, amount) CREATE INDEX product_category_idx ON :Product(category, price) // Use indexes in analytical queries MATCH (u:User) WHERE u.last_active_date >= datetime() - duration('P30D') AND u.registration_date <= datetime() - duration('P365D') RETURN COUNT(*) AS retained_users </code></pre></div> <h3 id="best-practices" class="position-relative d-flex align-items-center group"> Best Practices <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="best-practices" aria-haspopup="dialog" aria-label="Share link: Best Practices"> Share link </button> </h3> <h4 id="choosing-the-right-approach" class="position-relative d-flex align-items-center group"> Choosing the Right Approach <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="choosing-the-right-approach" aria-haspopup="dialog" aria-label="Share link: Choosing the Right Approach"> Share link </button> </h4><ul> <li>Use native graph algorithms for standard metrics (PageRank, community detection)</li> <li>Use vector search for semantic similarity and ML integration</li> <li>Use BM25 for keyword-based content search</li> <li>Combine approaches for hybrid analytics (graph + ML + search)</li> </ul> <h4 id="data-pipeline-integration" class="position-relative d-flex align-items-center group"> Data Pipeline Integration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="data-pipeline-integration" aria-haspopup="dialog" aria-label="Share link: Data Pipeline Integration"> Share link </button> </h4>Integrate Geode with your ML pipeline: <ol> <li>Feature Store Pattern: Store engineered features in Geode for real-time serving</li> <li>Online/Offline Consistency: Use same queries for batch training and online inference</li> <li>Incremental Updates: Use CDC to update ML models when graph changes</li> <li>A/B Testing: Use graph partitioning for controlled experiments</li> </ol> <h4 id="scalability-considerations" class="position-relative d-flex align-items-center group"> Scalability Considerations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="scalability-considerations" aria-haspopup="dialog" aria-label="Share link: Scalability Considerations"> Share link </button> </h4><ul> <li>Limit traversal depth in production queries (use explicit depth limits)</li> <li>Use property indexes to filter before traversal</li> <li>Cache frequently computed metrics (PageRank, centrality)</li> <li>Consider distributed mode for graphs with billions of edges</li> <li>Monitor query performance with EXPLAIN and PROFILE</li> </ul> <h3 id="further-reading" class="position-relative d-flex align-items-center group"> Further Reading <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="further-reading" aria-haspopup="dialog" aria-label="Share link: Further Reading"> Share link </button> </h3><ul> <li><a href="/tags/graph-algorithms/" >Graph Algorithms</a> - Built-in algorithm reference</li> <li><a href="/tags/vector-search/" >Vector Search</a> - HNSW and embeddings</li> <li><a href="/tags/bm25/" >BM25 Full-Text Search</a> - Text ranking and search</li> <li><a href="/categories/performance/" >Performance Optimization</a> - Query tuning</li> <li><a href="/tags/fraud-detection/" >Fraud Detection Patterns</a> - Graph-based fraud detection</li> <li><a href="/tags/anomaly-detection/" >Anomaly Detection</a> - Unusual pattern detection</li> <li><a href="/tags/community-detection/" >Community Detection</a> - Clustering algorithms</li> <li><a href="/tags/collaborative-filtering/" >Recommendation Systems</a> - Recommendation patterns</li> </ul>

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