kick start graph visualization projects

by Sbastien Heymann [email protected] Kick-start Graph Visualization Projects.

...with software. Co-founder of the Gephi project - 2008 Co-founder of the Linkurious startup - 2013 PhD 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 2008 Built to solve large graph visualization problems Latest 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.

A few words about me / Linkurious

Beautiful but unreadable pictures? Lets 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 used to 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 memory max 7 items otherwise the ability to make decisions drops Vision more 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

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

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 Lets choose node- link diagrams because its more common.

Design visualization Map data to visual variables. proximity hierarchy group

Expand Search Design interaction Add interactivity Details on demand Filter

Design visualization and interaction / Graph Viz 101 Learn more at http://linkurio.us/graph-viz-101

Use case 2. Bank loan fraud detection use case.

Use case / The cost of fraud $28.6B AITE 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? Lets 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 $25k Home address 58, Eisenhower Square Customer name J. Smith Phone number +33 5 68 98 25 74 Credit card 1 234$ ID J. 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 1787579953 SSN 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 days Investigate before transferring more money. Interaction Detect 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 visualized Relationships information is important Multiple node categories (address, phone, ..) -> node-link diagram -> icons or node colors by category Interactivity : yes Display node and rels information on demand Expand 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] Case studies: http://linkurio.us/blog Follow @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. Sbastien Heymann and Bndicte 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, Sbastien 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. Sbastien Heymann and Bndicte Le Grand. to appear in Hocine Cherifi (editor), Complex Networks and their Applications, Cambridge University Press. Gephi. Sbastien 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.