This document provides a summary of a webinar on deep link analysis using TigerGraph's native parallel graph database. The webinar demonstrated how TigerGraph can efficiently perform deep link analysis through multi-hop traversals of 6+ connections in real-time, which is critical for applications like fraud detection. It also showed TigerGraph's benchmark results outperforming other graph databases for 2-hop and 6-hop queries on a large Twitter dataset. Finally, the webinar included a demo of deep link analysis queries and circle detection at scale on TigerGraph.

1. Graph Gurus Episode 7
Connecting the Dots in Real-Time:
Deep Link Analysis with A Native Parallel Graph Database

2. © 2018 TigerGraph. All Rights Reserved
Welcome
● Attendees are muted but you can talk to us via Chat in Zoom
● Send questions at any time using the Q&A tab in the Zoom menu
● We will have 10 min for Q&A at the end
● The webinar will be recorded and sent via email
● A link to the presentation and reproducible steps will be emailed
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ZOOM ISSUES: update to the latest version of Zoom and if you are using
multiple monitors disable “use dual monitors” in settings

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Developer Edition Available
We now offer Docker versions and VirtualBox versions of the TigerGraph
Developer Edition, so you can now run on
● MacOS
● Windows 10
● Linux
Developer Edition Download https://www.tigergraph.com/developer/
3

4. © 2018 TigerGraph. All Rights Reserved
Today's Gurus
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Emma Liu
Product Manager
● BS in Engineering from Harvey
Mudd College, MS in Engineering
Systems from MIT
● Prior work experience at Oracle
and MarkLogic
● Focus - Cloud, Containers,
Enterprise Infra, Monitoring,
Management, Connectors
Huiting Su
Software Engineer
● Masters in Industrial
Engineering from Purdue
● Focus - Graph Algorithms and
Analytics, Machine Learning
● Resident GSQL Expert

5. © 2018 TigerGraph. All Rights Reserved© 2018 TigerGraph. All Rights Reserved
Agenda
● Graph Algorithms Overview
● What’s Link Analysis?
● Why Real-time Deep Link Analysis with TigerGraph?
● TigerGraph GSQL Graph Algorithms Library
● TigerGraph Differentiated Architecture
● TigerGraph Benchmark Report
● Demos for Graph Solution Use Cases
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Graph Algorithms, Part 3
Part 1 discussed PageRank (Graph Gurus Episode 5).
Part 2 discussed Community Detection (Graph Guru Episode 6)
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Picking the Right Graph Analytics Tools
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● Supervised learning:
look for particular patterns/features, then correlate to known
fraud
○ Pattern matching
○ Path finding, shortest path
● Unsupervised learning:
look for frequent/infrequent patterns & groupings
○ Community detection and clustering. Outliers.
○ Frequent subgraphs

8. © 2018 TigerGraph. All Rights Reserved
What is Link Analysis?
Stage One: Crawl - Finding
Relationship Among Entities (1
to 2 hops)
Stage Two: Walk - Go Across
Multi-Hop (3 to 4 hops)
Stage Three: Run - Real-time
Multi-Hop (6+ hop)
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9. © 2018 TigerGraph. All Rights
Reserved
8 Hop Deep Link Analysis Graph Pattern in Healthcare
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• Look for alternative connections
between prescriber/physician &
referred facilities
• Assign risk rating based on amount
of referral & strength of alternative
connections
• Compare billing across facilities to
identify “upcoding” / overbilling
• Improve efficiency for case
investigation for potential violations
with intuitive user interface
Dr. Thomas
Street Address
Administrator
Patient 1
John
Street Address
Patient 2
Susan
Patient 3
Ravi
Drug Rehabilitation
Center
Hop 1
Hop 3
Hop 5
Hop 6
Hop 7
Hop 8
Third Party Data
Source (Integrated
with internal data)
Claim
1
Claim
2
Claim
3
Hop 2
Claim
5
Claim
4
Claim
6
Hop 4

10. 10
Why Real-time Deep Link Analysis with
TigerGraph?

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Why Real-time Deep Link Analysis?
Drive actions with
timely insights
• Prevent Loss
• Example: Detect
Fraudulent Credit
Card Transaction
(Ride Sharing)
Capture business
moments
• Increase Revenue
• Example: Wish.com
Product
Recommendation
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• Exponential
Knowledge Growth
• Example: Social
Network Knowledge
Go deeper to find
new insights

12. © 2018 TigerGraph. All Rights Reserved
Common Link Analysis Questions
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RDBMS
• Real World Challenge:
• Not designed for
discovery/exploratory type of
analytics
• No easy SQL for answering these
questions
TigerGraph
• Real World Benefit:
• Designed for discovery/exploratory
type of Link analytics
• GSQL can naturally and efficiently
express and solve the query
Is there a connection between Customer A and Customer B?
What is the shortest path between Location A and Location B?
Is Driver A in the vicinity of Passenger A, and B around the same location B?

13. © 2018 TigerGraph. All Rights Reserved
NEW! Open Source GSQL Graph Algorithm Library
Each graph algorithm is a GSQL query
● May have zero or more input parameters
● Typically 3 variations:
○ Standard JSON output
○ Write to a CSV file
○ Save to vertex attributes (requires that the attributes
exist)
https://github.com/tigergraph/ecosys/tree/master/graph_algorithms
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Single-Source Shortest Path, Unweighted
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shortest_ss(VERTEX v, BOOL display)
shortest_ss_file(VERTEX v, BOOL display,STRING filepath)
shortest_ss_attr(VERTEX v, BOOL display)

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Single Source Shortest Path, Weighted
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shortest_path_any_wt(VERTEX v, INT maxDepth, BOOL display)
shortest_path_any_wt_file(VERTEX v, INT maxDepth, BOOL display, STRING filepath)
shortest_path_any_wt_attr(VERTEX v, INT maxDepth, BOOL display)

16. © 2018 TigerGraph. All Rights Reserved
All Pairs Shortest Path
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CREATE QUERY all_pairs_shortest(INT maxDepth, BOOL display, STRING fileBase)
{
Start = {Node.*};
Result = SELECT s FROM Start:s
POST-ACCUM
shortest_ss_any_wt_file(s, maxDepth, display, fileBase+s);
}

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Triangle Counting
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tri_count_fast()
tri_count_fast_file(FILE filepath)
tri_count_fast_attr()

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{
"error": false,
"message": "",
"version": {
"schema": 0,
"api": "v2"
},
"results": [
{"num_triangles": 4}
]
}
Triangle Counting Output

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How does a “Native Parallel Database”
Empower Deep Link Analysis?
TigerGraph

20. © 2018 TigerGraph. All Rights Reserved
TigerGraph Key
Differentiated Architecture
● Automatic computational parallelism
based on Massively Parallel
Processing (MPP)
○ Each node/edge = a unit of
storage + a computational unit
○ Each node/edge is processed
in parallel
Well-Designed Parallel Computation
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TigerGraph Empowers Real-time Deep Link Analysis
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TigerGraph engine
automatically scales the
computation across all
threads and CPU cores
available

22. 22
TigerGraph Benchmark
For Real-time Deep Link Analysis

23. © 2018 TigerGraph. All Rights Reserved
Twitter Dataset Benchmark - 2 Hop Path Query Time
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Measure TigerGraph Neo4j Neptune1 Neptune2 JanusGraph ArrangoDB.r ArrangoDB.m
Time (ms) 0.46 18.34 26.17 27.40 27.78 28.98 N/A
Normalized 1 40 57 60 60 63 N/A
Timeouts 0 0 63 60 48 101 N/A
Twitter Dataset: User-Follower Directed Graph (41.6M Vertices / 1.47B Edges)

24. © 2018 TigerGraph. All Rights Reserved
Twitter Dataset Benchmark - 6 Hop Path Query Time
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Measure TigerGraph Neo4j Neptune1 Neptune2 JanusGraph ArrangoDB.r ArrangoDB.m
Time (sec) 63.06 N/A N/A N/A N/A N/A N/A
Normalized 1 N/A N/A N/A N/A N/A N/A
% OOM 0 0 100% 100% 0 0 N/A
% Timeouts 0 100% 0 0 100% 100% N/A
Twitter Dataset: User-Follower Directed Graph (41.6M Vertices / 1.47B Edges)

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Why Deep Link Analysis in your Industry?

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Reserved
Use Case in Finance: Finding False Negatives in AML with Graph
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Flagged as a low risk AML alert as
the new customer has no financial
transaction history, no previous
alerts or SARs and is not in a high
risk geography
Elevated to a high risk AML alert as
the new account shares a phone
number with four customers who
have alerts turned into SARs
Traditional AML Solution Graph based AML Solution
1st
Hop
2nd
Hop
3rd
Hop
4th
Hop
5th
Hop

27. 27
Use Case Demo
Huiting Su, Solution Engineer

28. © 2018 TigerGraph. All Rights Reserved
Demo Overview
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● Demo Query 1: 2 hop traversal (“Walk”)
● Demo Query 2: 5 hop deep link analysis (“Run”)
● Demo Query 3: 3-6 hop circle detection - scalability (“Walk/Run”)

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Takeaways

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Summary
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● Real-time Deep Link Analysis is critical to businesses who need
Graph
● TigerGraph is designed from ground-up for deep link analysis
● GSQL Graph Algorithms for real-time deep link analysis
● Graph Solution Use Case Demo

31. Q&A
Please send your questions via the Q&A menu in Zoom
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32. © 2018 TigerGraph. All Rights Reserved
Episode 8:
DECEMBER 19 AT 11:00 A.M. PT / 2:00 P.M. ET
Location, Location, Location -
Geospatial analysis with a Graph Database
https://info.tigergraph.com/graph-gurus-8
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SEE MORE WEBINARS AT
https://www.tigergraph.com/w
ebinars-and-events/

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Additional Resources
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New Developer Portal
https://www.tigergraph.com/developers/
Download the Developer Edition or Enterprise Free Trial
https://www.tigergraph.com/download/
Guru Scripts
https://github.com/tigergraph/ecosys/tree/master/guru_scripts
Join our Developer Forum
https://groups.google.com/a/opengsql.org/forum/#!forum/gsql-users
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