Towards Reliable Social A/B Testing: Spillover-Contained Clustering with Robust Post-Experiment Analysis

A/B testing is the foundation of decision-making in online platforms, yet social products often suffer from network interference: user interactions cause treatment effects to spill over into the control group. Such spillovers bias causal estimates and undermine experimental conclusions. Existing approaches face key limitations: user-level randomization ignores network structure, while cluster-based methods often rely on general-purpose clustering that is not tailored for spillover containment and has difficulty balancing unbiasedness and statistical power at scale. We propose a spillover-contained experimentation framework with two stages. In the pre-experiment stage, we build social interaction graphs and introduce a Balanced Louvain algorithm that produces stable, size-balanced clusters while minimizing cross-cluster edges, enabling reliable cluster-based randomization. In the post-experiment stage, we develop a tailored CUPAC estimator that leverages pre-experiment behavioral covariates to reduce the variance induced by cluster-level assignment, thereby improving statistical power. Together, these components provide both structural spillover containment and robust statistical inference. We validate our approach through large-scale social sharing experiments on Kuaishou, a platform serving hundreds of millions of users. Results show that our method substantially reduces spillover and yields more accurate assessments of social strategies than traditional user-level designs, establishing a reliable and scalable framework for networked A/B testing.

Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles

Deep-learning-based super-resolution photoacoustic angiography (PAA) is a powerful tool that restores blood vessel images from under-sampled images to facilitate disease diagnosis. Nonetheless, due to the scarcity of training samples, PAA super-resolution models often exhibit inadequate generalization capabilities, particularly in the context of continuous monitoring tasks. To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images. We start by generating realistic PAA images of human lips from hand-drawn curves using a diffusion-based image generation model. Subsequently, we train a self-similarity-based super-resolution model with these forged PAA images. Experimental results show that our method outperforms the super-resolution model trained with authentic PAA images in both original-domain and cross-domain tests. Specially, our approach boosts the quality of super-resolution reconstruction using the images forged by the deep learning model, indicating that the collaboration between deep learning models can facilitate generalization, despite limited initial dataset. This approach shows promising potential for exploring zero-shot learning neural networks for vision tasks.