Networks for AB Testing

In this episode, the data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok.

We talk about how network science can enhance AB testing by accounting for complex social interactions, especially in environments where users are both viewers and content creators. These interactions might cause a "spillover effect" meaning a possible influence across experimental groups, which can distort results.

To mitigate this effect, our guest presents heuristics and algorithms they developed ("one-degree label propagation") to allow for good results on big data with minimal running time and so optimize user experience and advertiser performance in social media platforms.

Guest

Wentao Su: "Wentao is currently a TikTok Data scientist working on advertising measurement and before that he spent 4.5 years working at LinkedIn on A/B experimentation methodology design, representation metric development and other interesting metric measurement projects. Prior to that, he worked at Bank of America and JPMorgan Chase as quantitative finance analyst and model developer. He holds a Ph.D. in Economics and a B.S. in Computer Science. Wentao is particularly interested in A/B experiment design and published a paper this year in the KDD conference on egoCluster measurement to more accurately measure the network effect"