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Create and use graph visualizations efficiently in your projects.
...with software.
Co-founder of the Gephi project - 2008Co-founder of the Linkurious startup - 2013PhD in computer science, UPMC LIP6 - 2013
A few words about me
I democratise graph thinking (with pink titles)
makes graphs handy
Open source project started in 2008Built to solve large graph visualization problemsLatest version downloaded ~ 400,000 times
http://gephi.org
A few words about me / Gephi
makes graphs handy
A few words about me / Gephi
A few words about me / Linkurious
Started by a collaboration with Stanford - Mapping the Republic of Letters and DensityDesign in 2012.Now French startup of 3 people.
Linkurious helps companies make sense of data with user-friendly visualization software.
We help business analysts, R&D teams, developers and scientists.
Beautiful but unreadable pictures?Let’s make graph visualization useful.
0. Why?
1. Key takeaways a. The 5 questions b. User stories c. Design visualization + interaction
2. Fraud detection use case
3. Q&A
How to create and use graph visualization successfully?
Agenda
PRACTICE
PRACTICE
0. Why graph visualization?
Huh...
What is a graph?
This is a graph.
Father Of
Father Of
Siblings
What is a graph? / Nodes & relationships
A graph is a set of nodes linked by relationships.
Father Of
Father Of
Siblings
This is a node
This is a relationship
People, objects, movies, restaurants, music...
Antennas, servers, phones, people...
Supplier, roads, warehouses, products...
Graphs can be usedto model many domains.
Supply chains Social networks Communications
Differents domains where graphs are important
Graph visualization can help you in many ways.
Do you have a graph project?
“The greatest value of a picture is when it forces us to notice what we never expected to see.”
Why?
John Tukey (1962)
How to create and use graph visualization successfully?
1. Key takeaways to kick-start your projects.a. Ask 5 questions.b. Write user stories.c. Design visualization and interaction.
Ask 5 questions / Q1: Data, tadaa?
You need data.sourcing - cleaning - update
sensemaking - scale - complexity
Ask 5 questions / Q1: Data, tadaa?
Can you model data as graphs?
image: Martin Grandjean
Hypothesis discovery, evidence finding
Impact analysis, reportingData modelling, database administration
Set up your goal.
Administrate Understand Monitor
Ask 5 questions / Q2: Why using graph visualization in your project?
images: XKCD & the web
Ask 5 questions / Q3: Who will use it?
Define personas.
data scientist business analyst
developer public audience
images: PhdComics & Despicable Me
Short-term memorymax 7 items otherwise the ability to make decisions drops
Visionmore than 10 000 nodes is generally useless
Ask 5 questions / Q4: What are the constraints?
Acknowledge human limits.
50 nodes – 1B nodes Graph size
Machine performances
Server side VS client side rendering
Interactive VS print
Ask 5 questions / Q4: What are the constraints?
Acknowledge technical limits.
individual use VS collaborative work
artwork VS integrated into an application
Ask 5 questions / Q5: How is it used?
Define scope.
1. What are the data?
2. What is your goal?
3. Who is your end-user?
4. What are the constraints?
5. How is it used?
Ask 5 questions / Summary
The 5 questions
Ask 5 questions / Your turn!
Answer the 5 questions of your project.
PRACTICE
How to create and use graph visualization successfully?
1. Key takeaways to kick-start your projects.a. Ask 5 questions.b. Write user stories.c. Design visualization and interaction.
I define a data model.I generate a significant graph sample.I create a business query with Cypher.I visualize the query result.I iterate on the data model until it is satisfying.
Write user story / The developer story
“I am creating a Neo4j graph database for my application.”
Write user story / Your turn!
Write your own user story.
PRACTICE
How to create and use graph visualization successfully?
1. Key takeaways to kick-start your projects.a. Ask 5 questions.b. Write user stories.c. Design visualization and interaction.
Graph visualization in practice
Design visualization
How to represent graphs?
(a) Nodes are ordered as rows and columns; connections are indicated as filled cells. (b) A matrix representation of a typical biological pathway. in (Gehlenborg 2012)
Design visualization / Common graph representations
Matrices
(a) A directed graph typical of a biological pathway. (b) An undirected graph with nodes arranged in a circle. (c) A spring-embedded layout of data from b. in (Gehlenborg 2012)
Design visualization / Common graph representations
Node-link diagrams
Design visualization
Let’s choose node-link diagrams because it’s more common.
Design visualization
Map data to visual variables.
proximity hierarchy group
Expand
Search
Design interaction
Add interactivityDetails on demand
Filter
Design visualization and interaction / Graph Viz 101
Learn more athttp://linkurio.us/graph-viz-101
How to create and use graph visualization successfully?
1. Key takeaways to kick-start your projects.a. Ask 5 questions.b. Write user stories.c. Design visualization and interaction.
Use case
2. Bank loan fraud detection use case.
Use case / The cost of fraud
$28.6BAITE Group estimates that first party fraud will cost $28.6 billion in credit card losses a year by 2016.
http://news.alaric.com/industry-news/fraud/a-new-approach-to-first-party-fraud-reducing-bad-debt/http://bankinganalyticsblog.fico.com/2013/02/first-party-fraud-it-was-me.html
A criminal uses the fake identity to register a bank
account. He acts like a normal customer and tries to
secure a loan.
Once the criminal feels he cannot get access to more
money he carefully prepares his exit : in a short amount of
time he empties all of his accounts and disappears.
A criminal or a group of criminal mix pieces of
information (addresses, phone numbers, social
security number) to create a “synthetic-identity”.
A look at a common fraud scenario banks face.
Create a fake identity
Go to the bank, ask for a loan
Disappear with the money
Use case / A common fraud scenario
Use case / How do we set up a graph-based fraud detection system?
Let’s ask our 5 questions.
1. What are the data?
2. What is your goal?
3. Who is your end-user?
4. What are the constraints?
5. How is it used?
Use case / Q1: What are the data?
We model customer data as a graph.
Loan$25k
Home address58, Eisenhower Square
Customer nameJ. Smith
Phone number+33 5 68 98 25 74
Credit card1 234$
IDJ. Smith
A graph showing a legitimate customer and the information she is linked to.
Use case / Q1: What are the data?
In a fraud ring people share the same information.
58, Eisenhower Square
14, Roses Street
+33 6 75 89 22 14
$7k
P. Martin
$12,5k +331 42 58 66 00
J. Smith
SSN 17873897893
31195855
$20k
E. Selmati
SSN 1787576553
$45k
P. Smith
SSN 1787579953SSN 1267576553
31184274
Use case / Q2: What is your goal?
We want to detect fake customer identities.
She is a fraud expert but has limited data and computer skills.
She works with a team of data analysts for a large bank.
When an alert is triggered, she checks if the customer account belongs to a potential fraud ring.
Use case / Q3: Who is your end-user?
Our user is a fraud analyst.
image: PhdComics
Thousands of new loans per month.
Time: a few daysInvestigate before transferring more money.
InteractionDetect fraud rings by exploring the graph gradually.
Use case / Q4: What are the constraints?
We have a large graph on a single database.
Use case / Q5: How it is used?
The visualization is embedded in a business process.
Lifecycle events trigger security checks
A new customer opens an account
An existing customer asks for a loan
A customer skips a loan payment
A Neo4j Cypher query runs to detect patterns
An analyst visualizes the connections to make an informed decision.
Use case / The user story
The fraud teams acts faster and more fraud cases can be
avoided.
If something suspicious comes up, the analysts can use Linkurious to quickly assess the situation.
Linkurious allows the fraud teams to go deep in the data and build cases against fraud
rings.
Treat false positives
Investigate serious cases
Save money
Linkurious allows you to control the alerts and make sure your customers are not
treated like criminals.
Max 200 nodes visualizedRelationships information is importantMultiple node categories (address, phone, ..)-> node-link diagram-> icons or node colors by category
Interactivity : yesDisplay node and rels information on demandExpand node connections on demand
Use case / Visualization and interaction design
Design
Use case
Proof-of-concept demo with Linkurious.
Conclusion
Graph visualization can add a great value to your project, learn to leverage it.
Contact us to discuss your projects: [email protected] studies: http://linkurio.us/blogFollow @linkurious #GraphViz101
Q&A
3. Q&A
Detailed use case on our blog :
● Part 1 : http://linkurio.us/how-to-detect-bank-loan-fraud-with-graphs-part-1/● Part 2 : http://linkurio.us/how-to-detect-bank-loan-fraud-with-graphs-part-2/● Neo4j data set : https://www.dropbox.com/s/wk8k5r23syp6kbx/fraud%20detection.zip
GraphGist by Kenny Bastani : http://gist.neo4j.org/?github-neo4j-contrib%2Fgists%2F%2Fother%2FBankFraudDetection.adoc
Video demonstration : https://vimeo.com/76891393 (around the 12 minutes mark)
Graph Visualization 101: http://linkurio.us/graph-viz-101/
Resources
Resources
Research papers
Visual Analysis of Complex Networks for Business Intelligence with Gephi. Sébastien Heymann and Bénédicte Le Grand. to appear in the Proceedings of the 1st International Symposium on Visualisation and Business Intelligence, in conjunction with the 17th International Conference Information Visualisation (IV 2013 - VBI).
Gephi: an open source software for exploring and manipulating networks. Mathieu Bastian, Sébastien Heymann and Mathieu Jacomy. in Proceedings of the Third International AAAI Conference on Weblogs and Social Media (ICWSM'09), in American Journal of Sociology (2009), pp.361-362
Points of View: Bar Charts and Box Plots. M Streit and N Gehlenborg. Nature Methods 11(2):117 (2014).
Book chapters
Exploratory Network Analysis: Visualization and Interaction. Sébastien Heymann and Bénédicte Le Grand. to appear in Hocine Cherifi (editor), Complex Networks and their Applications, Cambridge University Press.
Gephi. Sébastien Heymann. to appear in the Encyclopedia of Social Networks and Mining (ESNAM), Springer.
Books
Exploratory data analysis. Tukey, J. W. (1977).
References
References
Linkurious technology
Cloud ready and open source based.
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