Beyond Leakage and Complexity: Towards Realistic and Efficient Information Cascade Prediction

• URL: http://arxiv.org/abs/2510.25348v1
• Date: Wed, 29 Oct 2025 10:06:08 GMT
• Title: Beyond Leakage and Complexity: Towards Realistic and Efficient Information Cascade Prediction
• Authors: Jie Peng, Rui Wang, Qiang Wang, Zhewei Wei, Bin Tong, Guan Wang,
• Abstract summary: Information cascade popularity prediction is a key problem in analyzing content diffusion in social networks.<n>We propose a time-ordered splitting strategy that chronologically partitions data into consecutive windows.<n>Second, we introduce Taoke, a large-scale e-commerce cascade dataset featuring rich promoter/product attributes.<n>Third, we develop CasTemp, a lightweight framework that efficiently models cascade dynamics through temporal walks.
• Score: 37.50536404287287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information cascade popularity prediction is a key problem in analyzing content diffusion in social networks. However, current related works suffer from three critical limitations: (1) temporal leakage in current evaluation--random cascade-based splits allow models to access future information, yielding unrealistic results; (2) feature-poor datasets that lack downstream conversion signals (e.g., likes, comments, or purchases), which limits more practical applications; (3) computational inefficiency of complex graph-based methods that require days of training for marginal gains. We systematically address these challenges from three perspectives: task setup, dataset construction, and model design. First, we propose a time-ordered splitting strategy that chronologically partitions data into consecutive windows, ensuring models are evaluated on genuine forecasting tasks without future information leakage. Second, we introduce Taoke, a large-scale e-commerce cascade dataset featuring rich promoter/product attributes and ground-truth purchase conversions--capturing the complete diffusion lifecycle from promotion to monetization. Third, we develop CasTemp, a lightweight framework that efficiently models cascade dynamics through temporal walks, Jaccard-based neighbor selection for inter-cascade dependencies, and GRU-based encoding with time-aware attention. Under leak-free evaluation, CasTemp achieves state-of-the-art performance across four datasets with orders-of-magnitude speedup. Notably, it excels at predicting second-stage popularity conversions--a practical task critical for real-world applications.

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