Neo4j Webinar 23 April, 2020 Neo4j has always been a geographically distributed company. Our employee count of 300+ people is spread over more than 20 countries. Consequently, remote collaboration is in our DNA. A Social Knowledge Graph can extract topics or moods from instant messaging to improve information sharing. It can also identify “lonely nodes” in times of remote working. In our latest webinar, we will demonstrate how we at Neo4j have leveraged our own technology to improve the efficiency of remote collaboration and avoid "lonely nodes". The webinar illustrates how instant messaging conversations (in this case from Slack) are used to analyze collaboration. And, finally, we will explain how companies can roll out a similar solution in less than two months.

1. How a Social Knowledge Graph
Improves Remote Collaboration
April 23, 2020

2. Abstract
2
Neo4j has always been a geographically distributed company. Our employee count of 300+
people is spread over more than 20 countries. Consequently, remote collaboration is in our
DNA.
A Social Knowledge Graph can extract topics or moods from instant messaging to improve
information sharing. It can help understand popular and/or missing skill sets. It can help
improve team cohesion. It can help understand Customer relationships.
In our latest webinar, we will demonstrate how we at Neo4j have leveraged our own
technology to improve the efficiency of remote collaboration. The webinar illustrates how
instant messaging conversations (in this case from Slack) are used to understand
collaboration. And, finally, we will explain how companies can roll out a similar solution in
less than two months.

3. 3
Introductions
Morgan Senechal
Consultant, UK
Neo4j
Niels de Jong
Consultant, Benelux
Neo4j
Jan Aertsen
Director PS, EMEA
Neo4j

4. • Introduction to Neo4j
• Towards more efficient (remote) collaboration
• Challenges in (remote) collaboration
• Knowledge graphs
• Slack Knowledge Graph Model
• Demo
• Application
• Technical aspects
• Implementation aspects
• Wrap up
4
Agenda

5. 5
About Neo4j

6. Neo4j - The Graph Company
The Industry’s Largest Dedicated Investment in Graphs
6
Creator of the Market Leading Neo4j Graph Database Platform
have launched a Neo4j Trial
76% of the
> 300* employees
HQ in Silicon Valley, and offices in London, Munich, & Malmo
~400* Global Enterprise Customers
(*) stats from December 2019

7. ● Separate department within Neo4j since 2017
● Created to ensure worldwide implementation assistance for Neo4j Customers
● Currently 40+ certified consultants (FY2017 15, FY2018 25, FY2019 40 , FY2020 60)
○ Average of 5+ years experience in the graph space
○ Average of 10+ years IT experience
● Most common technical skills:
○ database experts (graph and other): DBA, data modelling, query design & optimization
○ application development: both backend and UI, typically Java & JS
● Dedicated PMO and training group
● Dedicated Solutions team (packaged solution frameworks, reusable components)
● Both packaged services (pain point resolution) as fully managed projects
7
Neo4j Customer Services
The fastest route to graph success

8. 2010 2011 2012 2013 2015 2017
Introduced Cypher
- Leading language
for graph queries
First open source
GA version of a
property graph
database
O’Reilly Graph
Database —
first definitive
book for graph
professionals
Introduced
labels to simplify
graph modeling
openCypher.org
open sourced
Cypher query
language as de
facto standard
Industry’s
1st Graph Platform
Graph Algorithms
for data scientists
Developer’s Neo4j
Desktop
2014
Visual Graph
Query Browser
2016
Causal
Consistency for
Graphs
Neo4j—The Graph Innovator
2018 2019
O’Reilly
Graph
Algorithms
Book
Neo4j Aura
Neo4j Bloom visual discovery
Cypher for Apache Spark
Cypher for Gremlin
GQL Manifesto / ISO Standard
Creators of a new database category

9. More efficient
Remote Collaboration?
9

10. Remote is the “new normal”
10

11. At Neo4j, remote is in our DNA
11
Today
20+ countries
90+ locations
Rapid growth
end of 2017: 120
end of 2018: 180
end of 2019: 300

12. At Neo4j, remote is in our DNA
12

13. 13
Customer
Customer
Remote Working Challenges

14. Remote Working Challenges
14
1. It’s hard to know who works where and on what.
How do we facilitate locating skills and expertise?
2. We want to ensure cohesion
How do we promote a “connected nodes” culture?
3. How can we improve efficiency of virtual teams?
What skills are relevant in a specific context (e.g. Customer)?

15. Knowledge graphs
15

16. 16
Knowledge Graph

17. 17
Knowledge Graph

18. 18
Why use Knowledge Graphs?

19. 19
Knowledge Graphs and Neo4j
Why is Neo4j so well fit for knowledge graphs?
1. Ability to store/retrieve connections that provide WIDE context
2. Schema free, easily to extend/flexible model
3. Ability to easily bring in structured and unstructured data
4. Free text search capabilities
5. Ability to include taxonomies, ontologies, semantics, …
6. Vast library of algorithms to mine/enrich the knowledge graph

20. Graphs are Changing the World
The US space agency uses Neo4j for their
“Lessons Learned” database to connect
information to improve searchability
effectiveness in space mission.
By using context-aware search, Cisco
saves four million hours a year that are
now used to engage with more
prospects and close more deals.
Knowledge Graph for documentsKnowledge Graph for learnings
Some well known examples

21. 21

22. Slack Knowledge Graph
22

23. 23
Eat your own dog food

24. 24
What we originally did
We built a knowledge graph centered around Neo4j’s IM (Slack).
Can we use our knowledge graph to improve collaboration?

25. 25
Graph Model
From Slack:
- People
- Messages/Threads
- Reactions
- Channels
- Links/Attachments
Additionally:
- Skills
- Clients

26. 26
Translating Slack to Knowledge Graph
acme-internal ACME

27. 27
Another Example

28. Putting it all together
28

29. Existing Graph
What can we reuse?
- People, messages and channels
- Sentiment values on text and emojis
What can we add?
- Teams
MATCH (person:User)-[r:REPORTS_TO*]->(manager:User)
WHERE manager.name = "Emil"
RETURN *
29

30. Hierarchy of Teams
30
match (t:Team) with t
match (u:User)
where u.email = t.manager
merge (t)<-[:IS_MANAGER]-(u)
set u:Manager

31. 31
Flexible Schema
- Add the new types

32. Demo
32

33. Before we get started
33

34. 34
Anonymized demo data
● No real Neo4j employees
● No real Neo4j customers
● Sampled data set (partial/with gaps) so no representative nbrs
Real life: GDPR, data privacy, …
● Only public channel data loaded into application (no private chats)
● Only aggregate data exposed via application
● Actual Slack conversations not available for querying

35. 35
Demo
The application
1. Locating skills and expertise within the company.
2. What skills are relevant to which Customer
3. Info on clients/documents
Sample analytical queries
1. How has the mood changed for a specific team?
2. How has the mood changed in a specific location?
3. Which teams have experienced the biggest mood shift?
4. What are the clusters of people frequently talking?

36. Demo
36

37. 37

38. 38

39. 39

40. 40

41. GUI
41

42. 42
Demo
The application
1. Locating skills and expertise within the company.
2. What skills are relevant to which Customer
3. Info on clients/documents
Analytical queries
1. How has the mood changed for a specific team?
2. How has the mood changed in a specific location?
3. Which teams have experienced the biggest mood shift?
4. What are the clusters of people frequently talking?

43. 43
Emoji Sentiment in London
- Dashboards...
- One-shot queries...
- some visualizations...

44. 44
Total sentiment of the EMEA field team

45. 45
For people in the European offices,
which team’s sentiment has changed?

46. Which communities frequently chat?
46
Community nodes
generated based on
Louvain community
detection algorithm.

47. Team Interactions
47
match
(t1:Team)<--(:User)-[w:WROTE_WITH]-(:Use
r)-->(t2:Team) where id(t1) > id(t2)
with t1, t2, sum(w.weight) as weight
where weight > 100
merge (t1)-[:INTERACTS_WITH
{weight:weight}]->(t2)

48. 48
What you saw
● How to find the right skills (within a specific context) fast.
● Enable people to connect to peers that can help them achieve their goals.
● Understand rapidly the “virtual teams” around specific topics.
● Collect info to feed into a wider C360 or KM initiative.
● Measure activity/mood of wider company/team.
● Identify implicit communities (not just teams) of people working together.

49. Beyond
remote work
efficiency
49

50. 50
Other potential use cases
1. Collect rich contextual data for Customer 360 initiative
2. Collect rich contextual data for knowledge mgmt initiative
3. GDPR
4. Forensic analysis
5. Social network analysis: influencers, followers, topics analysis
https://neo4j.com/use-cases/

51. Technical deep dive
(Have a look behind the scenes)
51

52. 52
Let’s get technical
1. Architecture
2. Using kettle for data loading
3. Front-end
4. Scoring algorithm / Sentiment analysis
5. Graph data science

53. 53
Architecture

54. 54
ETL using Kettle
Kettle is used to load the data into
Neo4j Aura
Kettle is an open-source ETL tool
with Neo4j connectors
Data Sources:
● data dump of Slack files (JSON)
● Salesforce
● Google drive

55. Kettle workflow
55
The workflow is composed of 4 main parts:
1. Create a batch process, which is a “next”
iteration of the previous batch.
2. List all the input files or the one that has
failed in a previous run, and attach them to
the current batch.
3. Loop through the batch to load the files.
During the process, if the load of a file fails, we
add a Neo4j label to that file. Same goes once we
completed the load for a file. This way, we can
later on see which files failed, and which has been
loaded.

56. Front end
56
● Using a java framework so you can use the Neo4j java driver
● Vuejs because it’s modular and has a flexible development environment.
● It also separates the design from the code
Frontend code
Backend code

57. 57
Sentiment Analysis
Messages & Emojis

58. 58
Louvain community detection based on message-reply graph
projection.
Only a (small) subset of what is possible!
Graph Data Science

59. Implementation
59

60. 60
Implementation (2-3 months)
Week1
Discovery
● Project Kick-off
● Discovery
● Project Framework
● Requirements sign-off
Week 2-3
Design
● Architecture
● Workflow
● Data Model
● Ingestion Points
● Project Plan
Week ~3-4
Development
● KANBAN Board
● Custom Development
● Frequent Demo’s
● Unit Testing
● DEV Deployments
Week ~4-8
Validation
● Functional Testing in
PRE PROD or QA env.
● Workflow validation
● Performance Testing
● Release Candidate
● Complete DR ( if in
scope)
Week ~8-10
Go Live
● PROD Deployment
● End to end validations
● Runbook
● Open to Pilot Users
● Final Rollout
Week 11-12
Training & Support Handover
● Training (if in scope)
● Support Processes
● Key Documentation
Links

61. 61
Risks/hurdles/discussion points
● Getting to the data
● Anonymizing/sampling/protecting the data (GDPR, privacy, HR policies)
○ during project
○ after roll-out
● Narrowing down the objective. (Too much potential!)
● Front-end architecture and embedding

62. 62
Sizing/scaling
< 200 people: 5Gb - 1 year
> 1000 people: 25Gb - 1 year
> 10.000 people: 250Gb - 1 year

63. 63
Need help - reach out!
Morgan Senechal
Consultant, UK
morgan.senechal@neo4j.com
Niels de Jong
Consultant, Benelux
niels.dejong@neo4j.com
Jan Aertsen
Director PS, EMEA
jan.aertsen@neo4j.com

64. How a Social Knowledge Graph
Improves Remote Collaboration
April 23, 2020