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While mobile social apps have become increasingly important in people's dailylife, we have limited understanding on what motivates users to engage withthese apps. In this paper, we answer the question whether users' in-appactivity patterns help inform their future app engagement (e.g., active days ina future time window)? Previous studies on predicting user app engagementmainly focus on various macroscopic features (e.g., time-series of activityfrequency), while ignoring fine-grained inter-dependencies between differentin-app actions at the microscopic level. Here we propose to formalizeindividual user's in-app action transition patterns as a temporally evolvingaction graph, and analyze its characteristics in terms of informing future userengagement. Our analysis suggested that action graphs are able to characterizeuser behavior patterns and inform future engagement. We derive a number ofhigh-order graph features to capture in-app usage patterns and constructinterpretable models for predicting trends of engagement changes and activerates. To further enhance predictive power, we design an end-to-end,multi-channel neural model to encode temporal action graphs, activitysequences, and other macroscopic features. Experiments on predicting userengagement for 150k Snapchat new users over a 28-day period demonstrate theeffectiveness of the proposed models. The prediction framework is deployed atSnapchat to deliver real world business insights. Our proposed framework isalso general and can be applied to other social app platforms.

Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat