Mar 15–18, 2020

A graph neural network approach for time evolving fraud networks

Sriram Ravindran (Adobe Inc), Deepak Pai (Adobe), Shubranshu Shekhar (Carnegie Mellon University)
11:30am12:00pm Monday, March 16, 2020
Location: LL20A

Who is this presentation for?

Data scientists or analysts

Level

Intermediate

Description

Adobe has an e-commerce platform where it sells various subscription products like Creative Cloud, Photoshop and Stock. We encounter large volume of fraudulent activity such as card testing, trial abuse, seat addition among others. In this talk we will describe how we represent the data as a heterogenous graph and use state-of-art graph neural network based approach to identify fraud.

We will start by introducing various types of fraud in eCommerce. Then, we will introduce our data, talk about its characteristics and how we represent it as a heterogenous attributed graph. Next, we will introduce the audience to state-of-the-art literature on graph neural networks.

And finally, we will talk about our contribution, timeSAGE, in adding time sensitive random walks to graph neural networks. We showed on real data with millions of nodes and edges, that our method can

- Generate low dimensional node embeddings.

- Use these embeddings to predict links with high accuracy.

In our context, link predictions are used to identify fraudulent actors by asking questions like, “Are these two devices used by the same person?”, or, “Did the same user create two email ID’s to avail a trial service twice?” etc.

Prerequisite knowledge

Basic concepts of neural networks; mild knowledge of convolutional and recurrent neural networks.

What you'll learn

Automate feature extraction and generating low dimensional embedding for time evolving networks using graph neural networks.
Photo of Sriram Ravindran

Sriram Ravindran

Adobe Inc

Sriram Ravindran is a data scientist at Adobe where he is building a platform called Fraud AI. Fraud AI is a solution being designed to meet Adobe’s fraud detection needs. Prior to this, he was a graduate research student at University of California, San Diego where he worked on applying deep learning to EEG (brain activity) data.
Photo of Deepak Pai

Deepak Pai

Adobe

Deepak Pai is a manager of AI machine learning core services at Adobe, where he manages a team of data scientists and engineers developing core ML services. The services are used by various Adobe Sensei Services that are part of experience cloud. He holds a master’s and bachelor’s degree in computer science from a leading university in India. He’s published papers in top peer-reviewed conferences and have been granted patents.

Shubranshu Shekhar

Carnegie Mellon University

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