Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

中文翻译：

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可扩展的重新定义随机块模型

随机块模型（SBM）是一种广泛使用的统计网络表示模型，具有良好的可解释性、表达性、泛化性和灵活性，近年来在网络科学领域变得流行和重要。然而，学习给定网络的最优 SBM 是一个 NP-hard 问题。由于现有 SBM 模型及其学习方法的巨大计算开销，这导致 SBM 在大规模网络中的应用受到重大限制。降低 SBM 学习的成本并使其可扩展以处理大规模网络，同时保持 SBM 的良好理论特性，仍然是一个未解决的问题。在这项工作中，我们从模型重新定义的新角度解决了这一具有挑战性的任务。我们提出了一种新的重新定义的具有泊松分布的 SBM 及其块学习算法，可以有效地分析大规模网络。对人工和真实世界数据进行的广泛验证表明，我们提出的方法在准确性和可扩展性之间的合理权衡方面明显优于最先进的方法。1