Problem
Research questions and friction points this paper is trying to address.
causal discovery
concurrent events
incomplete event sequences
Resilient Concurrent Causal Discovery for Topological Event Sequences
🤖 AI Summary
该研究针对网络事件序列中的因果关系发现难题，提出了一种鲁棒的并发因果发现方法RCCD，通过引入注意力机制和自监督掩码重构框架来提高对缺失数据的鲁棒性和准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Resilient Concurrent Causal Discovery
influence-aware hyperedge causal attention
masked-based alternating causal optimization
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