1.
THEYEARS OFTHE GRAPH:
THE FUTURE OFTHE FUTURE IS HERE
George Anadiotis
Connected Data London Meetup, June 29th 2020

2.
ABOUT ME
 Working with data since 1992
 Graph since early 2000
 Databases
 Modeling
 Research
 Analysis
 Consulting
 Entrepreneurship
 Journalism

3.
THEYEAR OFTHE GRAPH:
THE GO-TO SOURCE FOR ALLTHINGS GRAPH
 Term and article
 * Published on ZDNet in January 2018
 * Before the hype
 Site
 * https://yearofthegraph.xyz/
 Newsletter
 * https://yearofthegraph.xyz/newsletter/
 Social Media
 * https://www.linkedin.com/showcase/43364427/
 * https://twitter.com/linked_do
 Graph Database Report
 * https://yearofthegraph.xyz/graph-database-report/

4.
WHAT IS GRAPH?
Graph Analytics |Knowledge Graphs | Graph DBs | Graph AI

5.
GRAPH ANALYTICS:
FROMTHE BRIDGES OF KÖNIGSBERGTO
MODERN DATA SCIENCE

6.
GRAPH ANALYTICS:
PATHFINDING AND GRAPH SEARCH ALGORITHMS
 Search
 * Explore a graph either for general
discovery or explicit search
 * Example: Locate neighbors
 Pathfinding
 * Explore routes between nodes
 * Example: Navigation
 Graph Algorithms: Practical Examples in Apache Spark
and Neo4j. Mark Needham, Amy E. Hodler. O'Reilly 2019

7.
GRAPH ANALYTICS:
CENTRALITY ALGORITHMS
 Centrality
 * Understand the roles of particular
nodes in a graph and their impact
on that network
 * Example: Find influence
 Graph Algorithms: Practical Examples in Apache Spark
and Neo4j. Mark Needham, Amy E. Hodler. O'Reilly 2019

8.
GRAPH ANALYTICS:
COMMUNITY DETECTION ALGORITHMS
 Community Detection
 * Identifying related sets to reveal
clusters of nodes, isolated groups,
and network structure.
 * Example: Fraud analysis
 Graph Algorithms: Practical Examples in Apache Spark
and Neo4j. Mark Needham, Amy E. Hodler. O'Reilly 2019

9.
GRAPH ANALYTICS: USE CASE
 Drug Discovery
 * Leading Pharma
 * Data on genes, proteins, etc
 * Identification of causal relationships

10.
KNOWLEDGE GRAPHS:
FROMTIM BERNERS LEETO GOOGLE AND BEYOND

11.
KNOWLEDGE GRAPHS:
GOOGLE, MEETTHE SEMANTICWEB
 From the Semantic Web to the world
 *The Web is a Graph, and Google based
its success on PageRank
 * Categorizing web content needs
metadata and semantics
 * Google adopted Semantic Web
technology, coined the term
Knowledge Graph
 * Besides Google’s Knowledge Graph,
everyone can have one
 * From Morgan Stanley to Average Jo
 * Personal Knowledge Graphs

12.
KNOWLEDGE GRAPHS:
IT’S ALL ABOUT SEMANTICS AND SCHEMA

13.
KNOWLEDGE GRAPHS:
KNOWLEDGE GRAPH = ONTOLOGY = AI
 Mark Hall, Executive Director at
Morgan Stanley
 *Traditional data modeling has
concerned itself primarily with the
capture and retrieval of data
 * Ontology concerns itself with a shared
understanding of what that data means
 * Before embarking on the AI-journey,
it’s critical to ensure you understand
and document your domain

14.
KNOWLEDGE GRAPHS: USE CASE
 Knowledge Graph for Search
 * Leading Retailer in DACH
 * 200Million+ MAU, 300K+ search requests
 * Improve coverage, response time, bottom-line

15.
GRAPH DATABASES:
LEVERAGING CONNECTIONS

16.
GRAPH DATABASES:
MINDTHE HYPE
 The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies
to Solve Complex Problems. Denise Gosnell, Matthias Broecheler. O'Reilly 2019

17.
GRAPH DATABASES:
WHAT ARETHEY? HOW DOYOU CHOOSE ONE?
 Operational vs. Analytical
 * Fully-fledged graph API
 * Operations & Analytics
 * Future-proof, integrated
 Native vs. Non-native
 *Designed as a graph database
 * Storing data in a native format
 * Optimized for graph

18.
PROPERTY GRAPH DATABASE USE CASES
 Operational
 applications
 Graph
 Analytics
 AI

19.
RDF GRAPH DATABASE USE CASES
 Data Integration  Knowledge Graph  AI

20.
GRAPH DATABASE: USE CASE
 Smart Home - IoT
 * LeadingTelco in the Nordics
 * 1,5 Million Homes
 * Real-time processing

21.
GRAPH AI:
THE FUTURE OFTHE FUTURE

22.
GRAPH AI:
MACHINE LEARNING
 Image: Oracle

23.
GRAPH AI:
GRAPH NEURAL NETWORKS
 Graph Neural Networks: A Review of Methods and Applications.
Zhou et. Al.
 Graph Neural Networks (GNNs)
 * Models that capture dependence
of graphs via message passing
between the nodes of graphs .
 * Unlike standard neural networks,
GNNs retain a state that can
represent information from its
neighborhood with arbitrary depth.
 * Domain knowledge can effectively
help a deep learning system
bootstrap its knowledge, by
encoding primitives instead of
forcing the model to learn these
from scratch.

24.
GRAPH AI:
GRAPH EMBEDDINGS
 Image: Oracle
 Graph Embeddings
 * Embeddings: reduce dimensions of
input to machine learning algorithms
 * Graph type data are discrete. Graph
embedding pre-processes graphs to
turn them into a continuous vector
space.
 * Walk embedding methods perform
graph traversals with the goal of
preserving structure and features
 * Proximity embedding methods use
Deep Learning methods and/or
proximity loss functions to optimize
proximity

25.
GRAPH AI: USE CASE
 Anti-Fraud in real-time
 * LeadingTelco in China
 * 600 Million Users
 * Compliance, trust

26.
GRAPHS ARE EVERYWHERE

27.
THEYEAR OFTHE GRAPH:
THE GO-TO SOURCE FOR ALLTHINGS GRAPH
 Term and article
 * Published on ZDNet in January 2018
 * Before the hype
 Site
 * https://yearofthegraph.xyz/
 Newsletter
 * https://yearofthegraph.xyz/newsletter/
 Social Media
 * https://www.linkedin.com/showcase/43364427/
 * https://twitter.com/linked_do
 Graph Database Report
 * https://yearofthegraph.xyz/graph-database-report/