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[2505.11228] Learning hidden cascades via classification
Summary
The paper presents a novel machine learning framework for inferring hidden statuses in social networks, enhancing the understanding of spreading dynamics through observable indicators.
Why It Matters
This research addresses the limitations of traditional models that assume full observability of individual statuses in social networks. By introducing a framework that leverages observable symptoms to infer hidden states, it provides a more accurate and scalable approach to studying phenomena like disease spread and information diffusion, which is crucial in various real-world applications.
Key Takeaways
- Introduces a partial observability-aware machine learning framework.
- Utilizes observable indicators to infer hidden statuses in networks.
- Outperforms existing methods like Approximate Bayesian Computation.
- Demonstrates effectiveness on both synthetic and real-world networks.
- Scales efficiently to large networks, making it applicable in diverse scenarios.
Computer Science > Social and Information Networks arXiv:2505.11228 (cs) [Submitted on 16 May 2025 (v1), last revised 19 Feb 2026 (this version, v4)] Title:Learning hidden cascades via classification Authors:Derrick Gilchrist Edward Manoharan, Anubha Goel, Alexandros Iosifidis, Henri Hansen, Juho Kanniainen View a PDF of the paper titled Learning hidden cascades via classification, by Derrick Gilchrist Edward Manoharan and 4 other authors View PDF HTML (experimental) Abstract:The spreading dynamics in social networks are often studied under the assumption that individuals' statuses, whether informed or infected, are fully observable. However, in many real-world situations, such statuses remain unobservable, which is crucial for determining an individual's potential to further spread the infection. While final statuses are hidden, intermediate indicators such as symptoms of infection are observable and provide useful representations of the underlying diffusion process. We propose a partial observability-aware Machine Learning framework to learn the characteristics of the spreading model. We term the method Distribution Classification, which utilizes the power of classifiers to infer the underlying transmission dynamics. Through extensive benchmarking against Approximate Bayesian Computation and GNN-based baselines, our framework consistently outperforms these state-of-the-art methods, delivering accurate parameter estimates across diverse diffusion settings while scaling ef...
