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Graph Data Science [clear filter]
Tuesday, April 21

11:45am EDT

The Graph Data Science Journey: From Analytics to AI
When do you use graphs for machine learning, what domains can they be used in, and how do you get started. Real world examples and use cases to show the steps from getting started with a knowledge graph through to graph native learning. Graphs - or information about the relationships, connection, and topology of data points - are transforming machine learning. We'll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI.

This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We'll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery.

This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.

avatar for Amy Hodler

Amy Hodler

Director, Graph Analytics & AI Programs, Neo4j
Amy is a network science devotee, AI and Graph Analytics Program Manager at Neo4j, and a co-author of the O'Reilly book, ""Graph Algorithms: Practical Examples in Apache Spark and Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and... Read More →

Tuesday April 21, 2020 11:45am - 12:25pm EDT
Room 1

1:30pm EDT

Identity Graph at Scale - Transforming Billions of Page views to Unique Identity Profiles in Publishing
Leveraging Neo4j, Meredith has been able to create an Enterprise wide Identity Graph with 12.3 Billion Nodes and 18.9 Billion Relationships that represents its Digital presence over the last 18 Months across 30+ brands. Neo4js Graph Algorithms enable unique Digital Profiles for Premium Ad Sales. Digital Marketing and Custom Audience Advertising is a focus of any Media Publisher. Meredith Corporation is leading the field of first party data retargeting using Neo4j for an in-house Identity Graph containing every digital cookie seen across 30+ brands and applications, including People, Entertainment Weekly,Real Simple, Eating Well, and All Recipes. With 100 million unduplicated consumers, the Meredith Database is the largest U.S. consumer database of any media company, and includes 7 in 10 women and 8 in 10 homeowners. The Identity Graph solution has leveraged 10's of Billions page views across multiple data streams to create a graph comprising of 12.2 Billion Cookies and 18.9 Billion Relationships between them to identify recurring individuals across domains and devices to improve Custom Audience Advertising and Profiling.


Ben Squire

Data Scientist, Meredith Corporation
Benjamin Squire is a Senior Data Scientist at Meredith Corporation. He has developed several successful POC's in Machine Learning, Custom Audience Advertising, and Identity + Profiling. His interest is in using new technologies and automation to bring the best content to the right... Read More →

Tuesday April 21, 2020 1:30pm - 2:10pm EDT
Room 1

2:15pm EDT

New: Neo4j's Graph Data Science Library
avatar for Alicia Frame

Alicia Frame

Senior Data Scientist, Neo4j
Alicia Frame is the lead data scientist at Neo4j. She works as part of the product management team to determine the product roadmap for Neo4j's graph algorithms library, and the strategy to grow Neo4j into a dominant analytics platform. In her role, she works closely with early adopters... Read More →

Tuesday April 21, 2020 2:15pm - 2:55pm EDT
Room 2
Wednesday, April 22

2:15pm EDT

Worst (And Best) Practices for Implementing Graph Data Science
avatar for Sören Reichardt

Sören Reichardt

Graph Analytics Engineer, Neo4j

Martin Junghanns

Graph Analytics Engineer, Neo4j

Wednesday April 22, 2020 2:15pm - 2:55pm EDT
Room 2

2:15pm EDT

Ending the Licit Opioid Crisis with Neo4j and Artificial Intelligence
Leveraging Neo4j, machine learning, and our Analytics Driven Targeting methodology our team was able to identify pharmacies and prescribers diverting opioids and other controlled substances. Ultimately this has led to restrictions against numerous medical professionals in the U.S.  

Our team has supported actions against numerous medical professionals in the U.S. Using Neo4j, machine learning, our Analytics Driven Targeting methodology, and bespoke diversion-specific risk factors we analyzed millions of prescription records and identified numerous prescribers, patients, and pharmacies diverting opioids and other controlled substances. Neo4j proved to be critical in our analysis because the relationships between entities proved to be some of our most valuable features in our predictive models. Additionally, Neo4j's graph engine, algorithms, and built-in scalability enabled us to analyze the massive amount of data rapidly.

Wednesday April 22, 2020 2:15pm - 2:55pm EDT
Room 3

3:30pm EDT

Molecules are Graphs! Lowering the Costs of Drug Discovery with Neo4j
Molecules are graphs! When you change part of this graph, swap one part out for another, add something in here, remove a little there you change how that molecule behaves and interacts with your body. This talk models alterations of molecular graphs as a network and applies it to drug discovery!

**Molecules are graphs!** The nodes are atoms and the edges are bonds. What happens when we take these graphs and their properties and put them in a graph database?

**Drug discovery is expensive**. The cost of producing a new drug is estimated to cost $2.6 Bn with a significant chunk of that cost coming from research and development of the drug molecule. Reducing down the cost of developing new therapeutics is key to helping patients. If researchers can make smarter decisions earlier in R&D the timelines and cost of bringing new drugs to patients will be reduced.

**Matched molecular pair** analysis (MMPA) is a method used in chemoinformatics that compares the properties of two molecules that differ only by a single chemical transformation (or graph alteration). An example of this would be if you were looking at two molecules that only differed by the substitution of a hydrogen atom for a fluorine atom. These two molecules whilst almost identical could have vastly different chemical properties. These **pairs** of compounds are known as **matched molecular pairs** (MMP), and any change in the properties of these molecules can be modelled on an edge linking them together.

This talk will explore how combining biological assay data with these chemical transformations in a **matched molecular pair knowledge graph (MMPKG)** using Neo4J allows for powerful exploration of chemical data. Through the use of Neo4j browser, cypher, and graph algorithms library new insights can be gathered to help answer the question on most medicinal chemists lips... _""which molecule should I make next?""_ and exactly how the MMPKG can be used and applied to real drug discovery problems to help drive this decision making process."

avatar for Matthew Sellwood

Matthew Sellwood

Product Manager, IQVia
Matthew is currently a Product Manager at IQVIA, and has worked across the life sciences and healthcare industry. He earned his Masters of Chemistry and his PhD at the University of Sheffield in the UK. In his thesis work he researched the discovery of novel therapeutics for ALS (Lou... Read More →

Wednesday April 22, 2020 3:30pm - 4:10pm EDT
Room 4

3:30pm EDT

Which Comes First, The Data Model or the Algorithm?
The intelligence community has applied link analysis to everything from modeling call records to financial transactions. But what happens when you apply the same techniques to the technical artifacts of cyberattacks? How do you avoid overthinking your data model when modeling such complex data?    

Cybersecurity may be the ideal domain for graph analysis as the relationships between technical attributes are often more critical than the discrete values. For example, an attribute's maliciousness often depends on the surrounding context. This can include the presence or absence of other attributes, the behaviors that those attributes exhibited, and the similarity of that behavior with other attack vectors. Graphs and contextual link analysis are very effective mechanisms for identifying potentially malicious activity.

However, before performing any type of analysis, you need to create the data model! While many graph data model examples are reasonably straightforward, the modeling of cybersecurity data can become quite complex. You would ideally model the attributes of any real world artifact (e.g., an email or file), the occasions in which those attributes were seen together, the behavior that those attributes exhibited when they were observed, and the source of your knowledge about those relationships. But how much knowledge do you really need to encode in the graph? When should you rely on path traversals rather than leveraging more advanced graph algorithms? Do you need to create a hyper graph in order to capture the source of relationships? What are the performance implications? How do you expire data from the graph? And finally, how do you make some decisions and actually build something?

avatar for Liz Maida

Liz Maida

CEO, UpLevel Security (McAfee)
Liz Maida is the Founder and CEO of Uplevel Security (recently acquired by McAfee). She was previously a Senior Director at Akamai Technologies and served in multiple executive roles focused on technology strategy and new product development. She played a lead role in Akamai’s initial... Read More →

Wednesday April 22, 2020 3:30pm - 4:10pm EDT
Room 2

4:15pm EDT

Graph Analytics in Anti-Money Laundering at Manulife
Money launderers use complicated schemes to wash dirty money, and financial institutes need to fight back with advanced techniques. Using graph analytics, it becomes feasible to connect dots in very complicated schemes that traditional methods cannot handle. Nowadays, money laundering involves leveraging different types of financial instruments with more complicated schemes. Preventing money laundering and terrorist financing has become high priority for financial institutions.

Traditional methods of monitoring for AML typically involve static, rule-based alerts built from previous experience. The biggest challenge faced by traditional approach is that money laundering schemes continuously changing in a way that is difficult to detect. With the power of graph analytics, it becomes feasible to connect dots in complicated schemes that traditional methods cannot handle. In this talk, we will share our experience using advanced rules traversing hidden patterns in the data, creating graph-based features for machine learning and finding similar patterns using graph embeddings.


Lin Gao

Data Scientist, Manulife

Wednesday April 22, 2020 4:15pm - 4:55pm EDT
Room 3