# Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps

arXiv: https://arxiv.org/abs/2006.06427 · HTML: https://mlsanity.com/paper/2006.06427/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 23 of 23)

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- [Link Prediction with Non-Contrastive Learning](https://mlsanity.com/paper/2211.14394/) (2022)
- [GiGL: Large-Scale Graph Neural Networks at Snapchat](https://mlsanity.com/paper/2502.15054/) (2025)
- [CARL-G: Clustering-Accelerated Representation Learning on Graphs](https://mlsanity.com/paper/2306.06936/) (2023)
- [Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation](https://mlsanity.com/paper/2109.11790/) (2021)
- [Graph Neural Networks: a bibliometrics overview](https://mlsanity.com/paper/2201.01188/) (2022)
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- [Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks](https://mlsanity.com/paper/2410.05416/) (2024)
- [DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://mlsanity.com/paper/2210.12402/) (2022)
- [TIM: Temporal Interaction Model in Notification System](https://mlsanity.com/paper/2406.07067/) (2024)
- [FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences](https://mlsanity.com/paper/2504.09428/) (2025)
- [H3GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning](https://mlsanity.com/paper/2504.11699/) (2025)
- [VISAGNN: Versatile Staleness-Aware Efficient Training on Large-Scale Graphs](https://mlsanity.com/paper/2511.12434/) (2025)
- [E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems](https://mlsanity.com/paper/2511.20564/) (2025)
- [Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training](https://mlsanity.com/paper/2602.01872/) (2026)
- [ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning](https://mlsanity.com/paper/2608.22786/) (2026)

## Abstract (arXiv)

With the rapid growth and prevalence of social network applications (Apps) in recent years, understanding user engagement has become increasingly important, to provide useful insights for future App design and development. While several promising neural modeling approaches were recently pioneered for accurate user engagement prediction, their black-box designs are unfortunately limited in model explainability. In this paper, we study a novel problem of explainable user engagement prediction for social network Apps. First, we propose a flexible definition of user engagement for various business scenarios, based on future metric expectations. Next, we design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement, namely friendships, user actions, and temporal dynamics to achieve explainable engagement predictions. FATE is based on a tensor-based graph neural network (GNN), LSTM and a mixture attention mechanism, which allows for (a) predictive explanations based on learned weights across different feature categories, (b) reduced network complexity, and (c) improved performance in both prediction accuracy and training/inference time. We conduct extensive experiments on two large-scale datasets from Snapchat, where FATE outperforms state-of-the-art approaches by ≈10% error and ≈20% runtime reduction. We also evaluate explanations from FATE, showing strong quantitative and qualitative performance.