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A lot of online marketing campaigns aim to promote user interaction. Theaverage treatment effect (ATE) of campaign strategies need to be monitoredthroughout the campaign. A/B testing is usually conducted for such needs,whereas the existence of user interaction can introduce interference to normalA/B testing. With the help of link prediction, we design a network A/B testingmethod LinkLouvain to minimize graph interference and it gives an accurate andsound estimate of the campaign's ATE. In this paper, we analyze the network A/Btesting problem under a real-world online marketing campaign, describe ourproposed LinkLouvain method, and evaluate it on real-world data. Our methodachieves significant performance compared with others and is deployed in theonline marketing campaign.

LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign