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Einstieg in Neo4j Graph Data Science
The document introduces Neo4j, a leader in graph data science, highlighting its role in leveraging connected data for various use cases such as fraud detection and real-time recommendations. It explains the importance of graph algorithms in analyzing relationships and structures within data, along with showcasing tools and libraries available for data scientists. In addition, it emphasizes how Neo4j can support more accurate predictions and insights through advanced graph analytics.
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Einstieg in Neo4j Graph Data Science
- 1. Neo4j, Inc. Allrights reserved 2021 Neo4j, Inc. All rights reserved 2021 1 Herzlich Willkommen! Einstieg in Neo4j Graph Data Science Alexander.Katzdobler@neo4j.com andrew.frei@neo4j.com
- 2. Neo4j, Inc. Allrights reserved 2021 2 Organisatorisches ○ Fragen während des Webinars werden zum Schluss behandelt und können gerne währenddessen per Chat gestellt werden. ○ Informationen zum Webinar werden im Nachgang an alle Teilnehmer versendet
- 3. Neo4j, Inc. Allrights reserved 2021 7/10 20/25 7/10 Top Life Science Firms Top Financial Firms Top Software Vendors Anyway You Like It Neo4j - The Graph Company The Industry’s Largest Dedicated Investment in Graphs 3 Creator of the Property Graph and Cypher language at the core of the GQL ISO project Thousands of Customers World-Wide HQ in Silicon Valley, offices include London, Munich, Paris & Malmo Industry Leaders use Neo4j On-Prem DB-as-a-Service In the Cloud
- 4. Neo4j, Inc. Allrights reserved 2021 Highly Valuable Connected Data Use Cases Drive Enterprise Adoption Network & IT Operations Fraud Detection Identity & Access Management Knowledge Graph Master Data Management Real-Time Recommendations 4
- 5. Neo4j, Inc. Allrights reserved 2021 Graph is the Fastest Growing DBMS Category, Neo4j is the Leading Player FASTEST GROWING CATEGORY MOST POPULAR WITH DEVELOPERS STRONGEST COMMUNITY Developers LinkedIn Skills 41k+ members with 220k+ Meetups 72k+ Members globally 5
- 6. Neo4j, Inc. Allrights reserved 2021 CAR DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Latitude: 37.5629900° Longitude: -122.3255300° Nodes • Can have Labels to classify nodes • Labels have native indexes Relationships • Relate nodes by type and direction Properties • Attributes of Nodes & Relationships • Stored as Name/Value pairs • Can have indexes and composite indexes • Visibility security by user/role Neo4j Invented the Labeled Property Graph Model MARRIED TO LIVES WITH O W N S PERSON PERSON 6
- 7. Neo4j, Inc. Allrights reserved 2021 7 Graph Databases Unlock Data-context for Richer Insights, Better Decisions, and Faster Innovation Shifting from Data-Driven to Intelligence-Driven Bought B ou gh t V i e w e d R e t u r n e d Bought K n o w s Knows Knows K n o w s Pl ay s Lives_in In_sport Likes F a n _ o f Plays_for People E.g., Employees, Customers, Suppliers, Partners, Influencers Transactions E.g., Risk management, Supply chain, Payments Knowledge E.g. Enterprise content, Knowledgebase, eCommerce content
- 8. Neo4j, Inc. Allrights reserved 2021 Which of the colored nodes would be considered the most ‘important'? D A B E H G F C I J K L M N 8
- 9. Neo4j, Inc. Allrights reserved 2021 Which of the colored nodes would be considered the most ‘important'? D D has the highest valence This is the most connected individual in the network. If importance is how well you are personally known, you pick D. Node G has the highest closeness centrality (0.52). Information will disperse through the network more quickly through this individual. If you need to get a message out rapidly, choose them. G Node I has the highest betweenness centrality (0.59). This person is an efficient connector of other people. Risk of disruption is higher if you lose this individual. I D A B E H G F C I J K L M N 9
- 10. Neo4j, Inc. Allrights reserved 2021 What is Graph data science? Graph Data Science is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predictions. Data scientists use relationships to answer questions.
- 11. Neo4j, Inc. Allrights reserved 2021 Why Graph data science? Relationships and network structures are highly predictive and underutilized – and already in your data. ● Relationships are the strongest predictor of behavior - James Fowler (“Connected”) Productionize more accurate and predictive models. Graphs are a natural way to store and use this predictive information, but different than what you’re doing today.
- 12. Neo4j, Inc. Allrights reserved 2021 Neo4j for Graph Data Science Neo4j Graph Data Science Library Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Neo4j Database Visual Graph Exploration & Prototyping Neo4j Bloom Practical way to harness the natural power of relationships and network structures to infer behavior Automated transformations with an integrated database built to store and protect relationships Explore results visually, quickly prototype and collaborate with different groups. 12
- 13. Neo4j, Inc. Allrights reserved 2021 13 Graphs & Data Science Knowledge Graphs Graph Algorithms Graph Native Machine Learning Find the patterns you’re looking for in connected data Use unsupervised machine learning techniques to identify associations, anomalies, and trends. Use embeddings to learn the features in your graph that you don’t even know are important yet. Train in-graph supervise ML models to predict links, labels, and missing data.
- 14. Neo4j, Inc. Allrights reserved 2021 When do I need Graph Algorithms? Query (e.g. Cypher/Python) Real-time, local decisioning and pattern matching Graph Algorithms Global analysis and iterations You know what you’re looking for and making a decision You’re learning the overall structure of a network, updating data, and predicting Local Patterns Global Computation
- 15. Neo4j, Inc. Allrights reserved 2021 Robust Graph Algorithms ● Compute connectivity metrics and learn the topology of your graph ● Highly parallelized and scale to 10’s of billions of nodes 15 The Neo4j GDS Library Mutable In-Memory Workspace Computational Graph Native Graph Store Efficient & Flexible Analytics Workspace ● Automatically reshapes transactional graphs into an in-memory analytics graph ● Optimized for analytics with global traversals and aggregation ● Create workflows and layer algorithms
- 16. Neo4j, Inc. Allrights reserved 2021 16 More, Better, Faster Algorithms Pathfinding & Search • Shortest Path • Single-Source Shortest Path • All Pairs Shortest Path • A* Shortest Path • Yen’s K Shortest Path • Minimum Weight Spanning Tree • K-Spanning Tree (MST) • Random Walk • Breadth & Depth First Search Centrality & Importance • Degree Centrality • Closeness Centrality • Harmonic Centrality • Betweenness Centrality & Approx. • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Hyperlink Induced Topic Search (HITS) • Influence Maximization (Greedy, CELF) Community Detection • Triangle Count • Local Clustering Coefficient • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • K-1 Coloring • Modularity Optimization • Speaker Listener Label Propagation Supervised Machine Learning • Node Classification • Link Prediction … and more! Heuristic Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors Similarity • Node Similarity • K-Nearest Neighbors (KNN) • Jaccard Similarity • Cosine Similarity • Pearson Similarity • Euclidean Distance • Approximate Nearest Neighbors (ANN) Graph Embeddings • Node2Vec • FastRP • FastRPExtended • GraphSAGE • Synthetic Graph Generation • Scale Properties • Collapse Paths • One Hot Encoding • Split Relationships • Graph Export • Pregel API (write your own algos)
- 17. Neo4j, Inc. Allrights reserved 2021 Graph Algorithms for Drug Discovery Identify drug mechanisms and new targets based on network structure PageRank to identify essential regulatory genes or drug targets Shortest path to link drug targets to possible outcomes or side effects Node Similarity to find structurally similar chemicals 17
- 18. Neo4j, Inc. Allrights reserved 2021 What: Finds important nodes based on their relationships Why: Recommendations, identifying influencers Features: - Tolerance - Damping 18 Page Rank
- 19. Neo4j, Inc. Allrights reserved 2021 19 Get started with Graph Data Science in Neo4j
- 20. Neo4j, Inc. Allrights reserved 2021 20 Neo4j GDSL Download
- 21. Neo4j, Inc. Allrights reserved 2021 21 Neo4j Desktop & Sandbox
- 22. Neo4j, Inc. Allrights reserved 2021 22 Neo4j Sandbox - GDS Playground
- 23. Neo4j, Inc. Allrights reserved 2021 23 Neo4j - Graph Algorithms Free Copy
- 24. Neo4j, Inc. Allrights reserved 2021 24 Neo4j - Graph Data Science for Dummies Free Copy
- 25. Neo4j, Inc. Allrights reserved 2021 25
- 26. Neo4j, Inc. Allrights reserved 2021 26