El camino hacia el éxito con las bases de datos de grafos, la ciencia de datos de grafos y la IA generativa

1. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. El camino hacia el éxito con las bases de datos de grafos, la ciencia de datos de grafos y la IA generativa Luis Salvador PreSales Engineer Iberia, Neo4j 1 Pierre Halftermeyer PreSales Engineer France, Neo4j

2. © 2023 Neo4j, Inc. All rights reserved. DATA GROWTH IS ACCELERATING. EVERYONE IS CONNECTED TO EVERYTHING 200+ Zettabytes In cloud data storage by 2025 65 Average number of enterprise SaaS apps 41B Connected IOT devices by 2025 2 2

6. © 2023 Neo4j, Inc. All rights reserved. Hybrid Workload Duality 6 Intelligent Applications Transactions - Security - Performance & Scalability - ACID Consistency - Intelligent Modeling - Extensive & Supported Algo Library - Scalable - Graph Visualization - Graph Transformations Graph Transactions Graph Analytics & Data Science

7. © 2023 Neo4j, Inc. All rights reserved. THE PROPERTY GRAPH: SIMPLY POWERFUL Employee City Company Nodes represent objects (nouns) Relationships are directional Relationships connect nodes are represent actions (verbs) Relationships can have properties (name/value pairs) Nodes can have properties (name/value pairs) name: Amy Peters date_of_birth: 1984-03-01 employee_ID: 1 :HAS_CEO start_date: 2008-01-20 :LOCATED_IN 7 7

8. © 2023 Neo4j, Inc. All rights reserved. 8 NATIVE GRAPH ARCHITECTURE: FAST, FLEXIBLE, SCALABLE Native Graph Database At write time Data is .connected. as it is stored At read time .Lightning-fast. retrieval of data and relationships via pointer chasing Relational Model Graph Model ACTED_IN ACTED_IN ACTED_IN Tom Cruise Mission Impossible Oblivion Person Movie Person-Movie Tom Cruise Top Gun Vs.

9. © 2023 Neo4j, Inc. All rights reserved. QUERY PERFORMANCE AS # OF JOINS INCREASE Connectedness and Size of Data Set Response Time Relational and other NoSQL databases Native Graph Database 1000x Advantage Minutes to milliseconds 5+ hops 3+ degrees Thousands of connections 0 to 2 hops 0 to 3 degrees Few connections 9 9

10. © 2023 Neo4j, Inc. All rights reserved. Analytics Tooling Graph Transactions Data Integration Dev. & Admin Drivers & APIs Discovery & Visualization Graph Analytics & Data Science Native graph technology for apps & analytics 10 Developers Admins Applications Business Users Data Analysts Data Scientists Bloom Kafka Connector BI Connector Spark Connector GRANDstack (GraphQL, React, Apollo, Neo4j) Data Warehouse Conn.

11. © 2023 Neo4j, Inc. All rights reserved. Developer Productivity: Rich tooling and easy onramp ops manager 11 data importer Visualize and explore your data Query editor and results visualizer Code-free data loader and modeler AuraWorkspace Unified Workspace

13. © 2023 Neo4j, Inc. All rights reserved. Enterprise-Grade: Security and Trust Built In Single Sign-On Secure Development Practices Dedicated VPC Role- & Schema-Based Access Control Encryption (At-Rest, In-Transit, and Intra Cluster) SOC 2 Type 1 13

16. © 2023 Neo4j, Inc. All rights reserved. 16 What’s in it for you: ● Improve model accuracy by 30% ● Simplify processes and remove headaches ● More projects into production without additional hiring Neo4j Graph Data Science Analytics Feature Engineering Data Exploration Graph Data Science Queries & Search Machine Learning Visualization

17. © 2023 Neo4j, Inc. All rights reserved. When do you need Graph Algorithms? Query (e.g. Cypher) 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

18. © 2023 Neo4j, Inc. All rights reserved. What’s important? Prioritization Who has the most connections? Who has the highest page rank? Who is an influencer? What’s unusual? Anomaly & Fraud Detection Where is a community forming? What are the group dynamics? What’s unusual about this data? What’s next? Predictions What’s the most common path? Who is in the same community? What relationship will form? 18 Pl ay s Lives_in In_sport Likes F a n _ o f Plays_for K n o w s Knows Knows K n o w s Graph Structure Improves Data Science Outcomes

19. © 2023 Neo4j, Inc. All rights reserved. 19 With The Largest Catalog of Graph Algorithms Pathﬁnding & Search Centrality & Importance Community Detection Supervised Machine Learning Heuristic Link Prediction Similarity Graph Embeddings …and more Graph algorithms are a set of instructions that visit the nodes of a graph to analyze the relationships in connected data.

20. © 2023 Neo4j, Inc. All rights reserved. 20 65+ Graph Data Science Techniques in Neo4j 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 • Node Regression … 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)

21. © 2023 Neo4j, Inc. All rights reserved. 21 Pathfinding Pathfinding and Graph Search algorithms are used to identify optimal routes, and they are often a required first step for many other types of analysis. Applications: Shortest path, Optimal paths, route availability, What-if analysis, Alternate Routing, Disaster Recovery https://neo4j.com/docs/graph-data-science/c urrent/algorithms/pathfinding/

22. © 2023 Neo4j, Inc. All rights reserved. 22 Centrality Finds important nodes based on relationships to other nodes in the graph Applications: Outlier detection, preprocessing, Inﬂuencer detection, Bridge points, points of failure, vulnerabilities Color and size represent inﬂuence based on centrality scores https://neo4j.com/docs/graph-data-science/c urrent/algorithms/centrality/

23. © 2023 Neo4j, Inc. All rights reserved. 23 Community Detection Evaluates how groups of nodes are clustered or partitioned, as well as their tendency to strengthen or break apart Applications: Recommendations, homogeneity, disjoint communities, outlier detection, preprocessing https://neo4j.com/docs/graph-data-science/c urrent/algorithms/community/

24. © 2023 Neo4j, Inc. All rights reserved. 24 Similarity Evaluates how alike nodes are at an individual level either based on node attributes, neighboring nodes, or relationship properties. Applications: Recommendations, What-if analysis, Disambiguation https://neo4j.com/docs/graph-data-science/c urrent/algorithms/similarity/

25. © 2023 Neo4j, Inc. All rights reserved. 25 Link Prediction These methods compute a score for a pair of nodes, where the score could be considered a measure of proximity or “similarity” between those nodes based on the graph topology. Applications: Context-enrichment, contact tracing, spam detection, association in social networks etc. https://neo4j.com/docs/graph-data-science/c urrent/algorithms/linkprediction/

26. © 2023 Neo4j, Inc. All rights reserved. 26 Embeddings A graph embedding is a way of representing each node in your graph as a ﬁxed-length vector. • Preserves key features • Reduces dimensionality • Can be decoded Different techniques may represent different aspects of a graph, and may use different approaches to learn that representation

29. © 2023 Neo4j, Inc. All rights reserved. GraphML: Regression & autotuning Node classiﬁcation: “What label should this node have?” Property regression: “What’s the value for this missing property?” Link Prediction: “Should there be a relationship between these 2 nodes?” f(x) f(x) f(x) We discover the best model for you - you just supply the data!

30. © 2023 Neo4j, Inc. All rights reserved. GDS example: Fraud Detection Graph algorithms for entity link analysis detect first party and synthetic identity fraud across channels in financial sector and other industries Detect patterns of fraudulent activity across channels Isolate fraudsters by running community detection, centrality and embedding algorithms Identify potential fraudsters by computing similarities between known fraudsters and customers Prevent fraud by flagging transactions and clients with higher risk

31. © 2023 Neo4j, Inc. All rights reserved. And made it seamless for all ecosystems and pipelines Graph Data Science BI & VISUALIZATIONS INGEST STORE PROCESS Apache Kafka MACHINE LEARNING Cloud Functions Neo4j Bloom PubSub DataProc Analytics Feature Engineering Data Exploration Graph Data Science Business Applications & Existing Systems Files (unstructured, structured) TensorFlow KNIME Python Cloud Storage AWS Lambda 31 Graph Database

32. © 2023 Neo4j, Inc. All rights reserved. © 2023 Neo4j, Inc. All rights reserved. What is Neo4j Bloom? Neo4j Bloom is our tool to Search, Explore and Discover graph data. Bloom’s no-code UI makes it the ideal interface for analysts to extract valuable information from knowledge graphs. Neo4j Product 💪 Visualize Large Graphs 🧠 Data Science Capabilities 📖 Natural Language Search

33. © 2023 Neo4j, Inc. All rights reserved. View the most well connected and influential nodes Recommendations from shared user interactions and associations Our Visualizations Make analysis easy to understand 33

36. © 2023 Neo4j, Inc. All rights reserved. Generative AI Is Predicted to Unlock In Economic Value The economic potential of generative AI: The next productivity frontier, McKinsey & Company, June 2023. $6.6 Trillion Up to 3.3% productivity improvement annually

39. © 2023 Neo4j, Inc. All rights reserved. LLM Hallucinations Definition: Language models generate text that is incorrect, nonsensical, or unreal. • Appear to answer questions confidently even if they don’t have facts • May provide contradicting or inconsistent responses to similar prompts

41. © 2023 Neo4j, Inc. All rights reserved. How to Help LLMs Do Better? Few-Shot Learning Fine-Tuning Grounding Provide completed examples “shots” to the AI as context in prompts. a.k.a In-Context Learning Provide additional training data to better tune GenAI to your use case Provide AI with the information to use for generating responses All of these are useful, but grounding is where Neo4j adds value Neo4j as the data source for Grounding

47. © 2023 Neo4j, Inc. All rights reserved. Neo4j Should be the Database for Grounding Vector DB Limitations Knowledge Graph Strengths Neo4j Differentiators Similarity ≠ Relevance or Accuracy Black-Box (Sub-Symbolic) Duplicate & incomplete results Missing reference information Challenging to answer multi-hop questions Difﬁcult for SME to correct Relevancy beyond just similarity Transparent symbolic representation Condensed information storage References between documents calculated before query time Enables human correction LLMs understand Cypher Vectors + Cypher Index for many data types (numeric, geopoints, dates) Fine-grained security and access control ACID transactions, high-availability, and scale Ecosystem integration Available on all clouds Graph Data Science for enhanced ML

48. © 2023 Neo4j, Inc. All rights reserved. API Knowledge Graph Neo4j AuraDS Graph Data Science Graph DB Intelligent Apps Knowledge Extraction and Ingestion Structured Unstructured Ontologies GCP Vertex AI Data Sources API Layer Customer Service Ticket Triaging Recommendations News Content & Discovery Enterprise Knowledge Search Patient Prioritization Clinical Decision Support Systems Pharmacovigilance Health Assistants FAQ Bots Knowledge Graph and Generative AI Reference Architecture GCP Document AI Bloom Generative AI Generative AI Dataproc Dataflow Pubsub

El camino hacia el éxito con las bases de datos de grafos, la ciencia de datos de grafos y la IA generativa

El camino hacia el éxito con las bases de datos de grafos, la ciencia de datos de grafos y la IA generativa

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El camino hacia el éxito con las bases de datos de grafos, la ciencia de datos de grafos y la IA generativa