Graph algorithms provide tools to extract insights from graph data. From detecting anomalies to understanding what are the key elements in a network or finding communities, graph algorithms reveal information that would otherwise remain hidden. Learn about: - The most popular graph algorithms and what they can be used for; - The benefits of using graph analytics with Linkurious Enterprise; - How to integrate graph analytics in Linkurious Enterprise.

1. SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
Graph analytics in
Linkurious Enterprise.

2. Why and how to use
graph analytics with
Linkurious
Enterprise.

3. Detect and investigate
sophisticated
threats faster with
Linkurious Enterprise.

4. Linkurious
Enterprise is only
for visualization
Linkurious
Enterprise can be
used to make
analytics available
to end-users
Misconception #1

5. Analysis of graph
data = graph
analytics
All sort of analytics
approaches can be
applied to graph
data
Misconception #2

6. Data analytics can
accelerate the
discovery of
interesting
information.

7. 3 different options
Graph
pattern-matching
Ex: Show all companies
connected to at least 2
directors connect to 1
address in the UK
Graph algorithms
Ex: community detection
(Louvain, label propagation),
centrality (PageRank,
closeness), shortest path
Other algorithms
Ex: linear regression, machine
learning, deep learning
21 3

8. Apply an iterative
approach to
maximize success.
Maturity
Visualization
and ad-hoc
investigations
Use of graph
patterns and
algorithms in
detection and
exploration
Use of other
data analytics
approaches
(ML, NLP)

9. What it looks like
from an
architecture
perspective.
Linkurious Enterprise
Graph DB
(OLTP)
Graph processor
(OLAP)
2. Persist results
1. Export data

10. Examples based on
the Paradise Papers
dataset.

11. Architecture
blueprint for graph
analytics with
Linkurious
Enterprise and
Neo4j.
Linkurious Enterprise
Neo4j + Neo4j Graph
Algorithms (plugin)
Business users
Developers, data
scientists

12. Graph analytics can
be used via filters,
sizes, colors, alerts,
query templates.

13. Graph
pattern-matching
example for an
alert.
//Identify addresses used to register more than 5 distinct companies
MATCH (a:Address)-[r]-(b)
WITH count(b) as score, a, COLLECT( distinct r) as r, COLLECT(
distinct b) as b
WHERE score>5
RETURN a, r, b, score

14. Graph
pattern-matching
example for a
query template.
//What other officers are connected to the same address or the same
company?
MATCH (a:Officer)
WHERE ID(a) = {{"My param":node}}
WITH a
MATCH
(a)-[rel3:OFFICER_OF|:SHAREHOLDER_OF]->(d:Entity)<-[rel4:OFF
ICER_OF|:SHAREHOLDER_OF]-(e:Officer)
RETURN a, d, e, rel3, rel4

15. Graph algorithm
example:
PageRank
algorithm.
//Computation of PageRank
CALL
algo.pageRank(null,null,{write:true,writeProperty:'pagerank_g'})

16. PageRank
algorithm used in
an alert.
//Detect French entities with a high PageRank
MATCH (a:Entity)-[r]-(b)
WHERE b.countries = "France"
WITH a.pagerank as score, a, COLLECT( distinct r) as r, COLLECT(
distinct b) as b, count(b) as degree
RETURN a, score, a.name as name, r, b, degree
ORDER BY score DESC

17. Graph algorithm
example:
community
detection.
//Detection of communities with the Louvain algorithm
CALL algo.louvain(
'MATCH (p:Officer) RETURN id(p) as id',
'MATCH
(p1:Officer)-[:OFFICER_OF]->(:Entity)<-[:OFFICER_OF]-(p2:Officer)
RETURN id(p1) as source, id(p2) as target',
{graph:'cypher',write:true});

18. Community
detection used in
a query template.
//Retrieve the nodes who belong to the same community
MATCH (a:Officer)
WHERE ID(a) = {{"My param":node}}
WITH a
MATCH p = (a:Officer)-[*..4]-(b:Officer)
WHERE a.communityLouvain = b.communityLouvain
RETURN p

19. Unlock the value of
your graph data
with Linkurious
Enterprise.

20. We work with more than 50+ organizations worldwide.

21. The compliance team
of BforBank uses
graph pattern search
to detect fraud.

22. Identifying criminal
communities based
on communication
patterns.

23. Using machine
learning to identify
suspicious
transactions.

24. www.linkurio.us
contact@linkurio.us