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Overview for graph processing

People You May Know: Fast Recommendations Over Massive Data

This discussion presents the evolution “People You May Know” (PYMK) to its current architecture. The focus is on various systems built along the way, with an emphasis on systems built for LinkedIn most recent architecture, namely Gaia, a real-time graph computing capability, and Venice an online feature store with scoring capability, and how LinkedIn integrates these individual systems to generate recommendations in a timely and agile manner, while still being cost-efficient. 

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Scaling Geographic Analytics with Azure Databricks and Spark GraphX

Devon Energy has developed a solution that analyzes subsurface attributes and readings in order to predict the drill’s position. This solution which is built on Azure Databricks, scales to over a thousand cores in order to process a full well in one to two hours and uses graph and statistics libraries to help maximize oil and gas production.

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Graph Representation Learning to Prevent Payment Collusion Fraud

PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, Venkatesh Ramanathan a data scientist at PayPal presents how advanced graph processing and machine learning algorithms are applied at PayPal for fraud prevention.

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Data science in practice: Examining events in social media

Jennifer Webb explains how cloud-based marketing company SuprFanz uses data science techniques and graph theory with Neo4j to generate live event attendance from social media platforms, email, and SMS to gain better insight into the behavior of music fans, event guests, and target audiences and predictive analytics to optimize promotional efforts. She also offers an overview of the unique technologies SuprFanz uses to drive traffic to events via social media.

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A Practical Use of Artificial Intelligence in the Fight Against Cancer by Brian Dolan

Artificial Intelligence is an important topic in the fight against cancer. Clinical Trails are at the frontier of innovation. Brian Dolan discusses techniques, data sets and platforms they use at Deep 6 to bring patients to clinical trials. The focus is on practical, repeatable methods he has developed at MySpace, Greenplum, UCLA and the US Intelligence Community.

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Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:

PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, Venkatesh Ramanathan a senior data scientist at PayPal   presents how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. He elaborates on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. He also explains how they employ sophisticated machine learning tools – open source and in-house developed.

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