Characterizing and Forecasting User Engagement with In-App Action Graph

Abstract

While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formalize individual user's in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement. Our analysis suggested that action graphs are able to characterize user behavior patterns and inform future engagement. We derive a number of high-order graph features to capture in-app usage patterns and construct interpretable models for predicting trends of engagement changes and active rates. To further enhance predictive power, we design an end-to-end, multi-channel neural model to encode temporal action graphs, activity sequences, and other macroscopic features. Experiments on predicting user engagement for 150k Snapchat new users over a 28-day period demonstrate the effectiveness of the proposed models. The prediction framework is deployed at Snapchat to deliver real world business insights. Our proposed framework is also general and can be applied to other social app platforms 1 .

Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

“…Most of the progress has been done in the context of static networks, with some advances being extended to dynamic networks. Particularly GNNs have been used in a wide variety of disciplines such as chemistry [61], [62], recommender systems [63], [64] and social networks [65], [66].…”

Friend Story Ranking with Edge-Contextual Local Graph Convolutions

“…Therefore, simply averaging link representations of all friends could lead to noisy representations by considering many inactive friendships. Even though user-user interaction can be reflected by edge features, explicitly penalizing less communicated friends when aggregating has still been shown as beneficial [32,49]. To appropriately characterize important friends by penalizing selected link representations when learning representations for intra-ego relations, we use a self-attention mechanism [51,52] to assign friends among intra-ego relations different importance.…”

GDLL: A Scalable and Share Nothing Architecture Based Distributed Graph Neural Networks Framework

“…The past few years have seen notable advancement in GNNs, in solving problems from diverse domains e.g., social network analysis [16], computer vision [17], chemistry [18], medicine [19], health [20], etc. However, deploying GNNs against big graph is challenging [21].…”