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Saturday, October 20 • 3:00pm - 3:30pm
Machine Learning on Social Networks

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Social networks are extremely common in our modern-day world. These networks are composed of individuals and connections, where connections here represent social interactions such as friendship, peer or work relationships, and communication. Social network data can originate from many sources including online social networks, mobile phones, email, financial transactions or a variety of digital communications channels over which individuals interact. Social networks are typically very large and heterogeneous, and while the construction of features from this data can be quite complex it can often justify itself by providing great signal for machine learning prediction tasks.

In this presentation I will describe the social networks that I work with as a data scientist in Tala, how we construct these networks from our various data sources and how we use these networks for machine learning tasks including credit scoring and fraud prediction. I will talk about the complexities and nuances of using this data, and considerations and challenges that arise when building live scoring machine learning models based on this data. I will describe local network features (based on individuals' direct connections) and global network features (based on an individual’s position or centrality within the network), and approximation methods for global metrics which allow for their rapid implementation in live models. I will discuss the effectiveness of models based on network features and conclude with other use cases of networks in data science.

avatar for Peter Fennell

Peter Fennell

Data Scientist, Tala
peter fennell is a Senior Data Scientist at Tala, where he uses a suite of tools including EDA, statistics, machine learning and engineering to build credit and fraud models for our global markets.Previous to Tala peter fennell was a postdoctoral researcher in statistics, networks... Read More →

Saturday October 20, 2018 3:00pm - 3:30pm
Ballroom # 403B