面向拓扑事件序列的鲁棒并发因果发现
Resilient Concurrent Causal Discovery for Topological Event Sequences
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中文总结 AI 辅助
针对拓扑事件序列因果发现中并发事件因果关系难捕捉、对不完整序列鲁棒性差的问题，提出RCCD方法，经实验验证其在电信网络数据集上优于现有SOTA方法。
中文摘要 AI 辅助
拓扑事件序列的因果发现对于保障网络可靠性至关重要，但现有方法难以捕捉并发事件产生的复杂因果关系，且对不完整事件序列缺乏鲁棒性。为解决这些问题，我们提出一种名为RCCD的鲁棒并发因果发现方法，可从拓扑事件序列中鲁棒学习因果图。具体而言，我们首先引入感知影响的超边因果注意力机制，将事件持续时间融入嵌入表示，通过超边因果卷积聚合并发事件特征，并注入网络先验知识以捕捉复杂的多对一因果交互。此外，我们设计了基于掩码的交替因果优化框架，该框架通过自监督掩码重构迫使模型基于上下文恢复掩码事件类型，从而提升预测器对缺失数据的鲁棒性。为验证方法有效性，我们在模拟及真实电信网络数据集上开展大量实验，结果表明，所提方法在准确性和鲁棒性上均显著优于现有最先进方法，更适用于真实电信网络环境。
英文摘要
Causal discovery on topological event sequences is crucial for ensuring the reliability of networks. However, existing methods struggle to capture the complex causal relationships arising from concurrent events and lack robustness to incomplete event sequences. To address these issues, we propose a resilient concurrent causal discovery method, termed RCCD, enabling robust learning of causal graphs from topological event sequences. Specifically, we first introduce an influence-aware hyperedge causal attention mechanism, which incorporates event duration into the embedding representation, aggregates concurrent event features via hyperedge causal convolution, and injects network prior knowledge to capture the complex many-to-one causal interactions. Furthermore, we design a masked-based alternating causal optimization framework, which forces the model to recover masked event types based on context through self-supervised mask reconstruction, thereby enhancing the resilience of the predictor to missing data. To validate the effectiveness of our method, we conduct extensive experiments on both simulated and real-world telecommunication network datasets. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in both accuracy and robustness, making it more suitable for real-world telecommunication network environments.
发表机构
- School of Software Technology, Dalian University of Technology（大连理工大学软件学院）
- School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）
- School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
- School of Computer Science and Artificial Intelligence, Liaoning Normal University（辽宁师范大学计算机科学与人工智能学院）
- University of Natural Resources and Life Sciences (BOKU)（自然资源与生命科学大学（BOKU））
- Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）
- College of Computing & Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。
