Si deseas obtener más información sobre la tecnología de grafos, este webinar introductorio es perfecto para comenzar a explorar el potencial de las relaciones de datos para tu negocio

1. Neo4j, Inc. All rights reserved 2021
Introducción a Neo4j
Paolo Délano – Solutions Architect
Miguel García - Sales Executive

2. Neo4j, Inc. All rights reserved 2021
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Agenda
1. ¿Quién es neo4j?
2. El concepto del grafo
3. ¿Porque usar neo4j?
4. Nuestra plataforma
5. Data Science con neo4j
6. Demo

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¿Quién es neo4j?

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Ayudamos al mundo a dar sentido a los datos !
Creador de Property Graph
y Cypher, lenguaje principal
y corazon del proyecto GQL
ISO
Miles de clientes en todo el
mundo
HQ en Silicon Valley, con
oficinas en Londres,
Munich, Paris & Malmo
Líderes de la industria con Neo4j
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+0KI9iJD
Flexibilidad para
implementación
On-Prem
DB-as-a-Service
En la nube
20 of 20 USA Top Financial Institutions
9 of 10 Top High Tech Companies
7 of 10 Top Retailers
8 of 10 Top Insurance Companies
8 of 10 Top Automakers
3 of 5 Top Hotels
7 of 10 Top Telecoms
Global Governments - Civilian, Defense
and Intelligence using Neo4j EE to
Analyze, Optimize & Protect

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Neo4j - #1 Categoría de Grafos

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Modelos de implementación flexibles
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SaaS completamente
administrado, precios
basados en el consumo
Nube nativa
Implementación de autoservicio
Sin acceso a la infraestructura y
sistemas subyacentes
Permita que los expertos de
Neo4j los atiendan
Modelo de implementación y
niveles de servicios
personalizables.
Operar en centro de datos
propios o bien nubes privadas .
Database-as-a-Service
Cloud Managed
Services (CMS)
Para nube privada, hibrida o
migración (lift-and-shift)
Traiga su propia licencia
Control total de su entorno
Disponible en cualquier nube con
tu/cuenta
Self-hosted

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Todas las regiones en el mundo
soportadas
Los datos se mantienen locales
en cada región
Facturación integrada y uso de sus
compromisos en la nube a través de
“Cloud Marketplace”
Disponibilidad Global
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Early Access
Program
Coming
Q4 2022

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El concepto del grafo

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Qué es un grafo?
• Un grafo es un conjunto de objetos discretos, donde cada una tiene algún
conjunto de relaciones con otros objetos
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Seven Bridges of Konigsberg problem. Leonhard Euler, 1735

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Cualquier cosa puede ser un grafo
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The internet A water molecule
H
O
H
The metro
Social media

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¿Porque usar neo4j?

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Neo4j está diseñado para almacenar data conectada
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TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data Connections in data
Real time storage & retrieval Real-Time Connected Insights
Long running queries
aggregation & filtering
“Our Neo4j solution is literally thousands of
times faster than the prior MySQL solution,
with queries that require 10-100 times less
code”
Volker Pacher, Senior Developer
Up to
3
Max
# of
hops
1 Millions

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RDBMS vs Modelo de Grafo
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Relational Model Graph Model
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person Friend
Person-Friend
ANDREAS
DELIA
TOBIAS
MICA

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Networks of People Transaction Networks Knowledge Networks
E.g., Risk management, Supply
chain, Payments
E.g., Employees, Customers,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
Las conexiones entre los datos son tan valiosas como la
data en si
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Highly valuable connected data drives enterprise adoption
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Network &
IT Operations
Fraud
Detection
Identity & Access
Management
Knowledge
Graph
Master Data
Management
Real-Time
Recommendations

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Labeled Property Graph Model
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CAR
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
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
LOVES
LIVES WITH
PERSON PERSON
LOVES

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La Plataforma

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Graph Transactions Graph Analytics &
Data Science
Tecnología de grafo nativa para aplicaciones y analísis
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Graph Transactions
Dev.
& Admin
Drivers & APIs
Developers
Admins
Applications
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Graph Analytics &
Data Science
GRANDstak
(GraphQL, React,
Apollo, Neo4j)
Tecnología de grafo nativa para aplicaciones y analísis

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Analytics
Tooling
Graph Transactions
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics &
Data Science
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
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Bloom
GRANDstak
(GraphQL, React,
Apollo, Neo4j)
Tecnología de grafo nativa para aplicaciones y analísis

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Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics &
Data Science
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Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Bloom
Kafka Connector BI Connector
Spark Connector
GRANDstak
(GraphQL, React,
Apollo, Neo4j)
Tecnología de grafo nativa para aplicaciones y analísis

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• Operational workloads
• Analytics workloads
Real-time Transactional
and Analytic Processing • Interactive graph exploration
• Graph representation of data
Discovery and Visualization
• Native property graph model
• Dynamic schema
Agility
• Cypher - Declarative query language
• Procedural language extensions
• Worldwide developer community
Developer Productivity
• 10x less CPU with index-free adjacency
• 10x less hardware than other platforms
Hardware efficiency
Performance
• Index-free adjacency
• Millions of hops per
second
Neo4j: Beneficios de la plataforma
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Graph Data Science
con Neo4j

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Qué es GDS?
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Data science when relationships matter

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Queries Algorithms Embeddings
Local
Matching
Global
Patterns
Graph
Representations
Use when
Caveat
Intent
You know the exact
relationships and data
you’re looking for
Overly-complicated global
queries can spin
Real-time local matching
for specific questions
You know the kinds of
patterns you’re looking for
Choosing the right
algorithm & parameter
tuning takes consideration
Find global patterns and
trends
You know there’s significant
data in the graph but it’s
unclear what to look for
Results aren’t human
readable or interpretable
Create a highly predictive graph
format for machine learning
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6 Categorías de Algorimos para Grafos
Pathfinding
and Search
Centrality Community
Detection
Heuristic
Link Prediction
Similarity
Determines the
importance of
distinct nodes in
the network.
Detects group
clustering or
partition.
Evaluates how
alike nodes are
by neighbors and
relationships.
Finds optimal
paths or
evaluates route
availability and
quality.
Estimates the
likelihood of
nodes forming a
future
relationship.
50+ graph algorithms in Neo4j
Embeddings
Learns graph
topology to
reduce
dimensionality
for ML
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60+ Algoritmos para Grafos en Neo4j
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• Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Triangle Count
• Clustering Coefficients
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• Balanced Triad (identification)
• 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
• Triangle Count
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Euclidean Distance
• Cosine Similarity
• Node Similarity (Jaccard)
• Overlap Similarity
• Pearson Similarity
• Approximate KNN
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
... Auxiliary Functions:
• Random
graph generation
• Graph export
• One hot encoding
• Distributions & metrics
Embeddings
• Node2Vec
• Random Projections
• GraphSAGE

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La Evolución de Graph Data Science
Decision
Support
Graph Based
Predictions
Graph Native
Learning
Graph Feature
Engineering
Graph
Embeddings
Graph
Networks
Knowledge
Graphs
Graph
Analytics
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Neo4j is a graph database
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Algoritmos para Detección de Fraude
Graph algorithms enable reasoning
about network structure
Louvain to identify communities
that frequently interact
PageRank to measure influence
and transaction volumes
Connected components
identify disjointed group
sharing identifiers
Jaccard to measure account
similarity

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Demo

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Thank you!
Questions?
luis@neo4j.com