Topological-Temporal Convolution Transformer Hawkes Process for Causal Structural Learning in Telecom Networks

Accurate causal discovery in telecommunication alarm event sequences is crucial for reliable root cause analysis, but presents significant challenges due to complex topological dependencies and inherent alarm redundancy. To address these challenges, we propose the topological-temporal convolution transformer Hawkes process (TTCTH), a novel framework for learning hidden causal relationships in alarm event sequences. To start with, a topological-temporal graph convolution is designed to initialize a causal graph for mainly exploring local dependencies among event types in the topological and temporal domains. We next design three separable transformer variants of topological transformer (TopoT), causal transformer and temporal transformer, which are sequentially stacked to globally capture causal dependencies among event types. In particular, the TopoT fuses prior topological information into alarm sequence representation. The causal transformer models causal dependencies with self-attention mechanism to capture spatially causal relations among event types. The temporal transformer is developed to model long-range causal dependencies across multiple events. We then leverage the high-dimensional hidden state representation, encoded by the stacked transformers, to construct feature evolution with the constraint-based Hawkes process. In order to observe the performance of TTCTH, we conduct extensive experiments on real-world alarm datasets. It demonstrates TTCTH’s excellence in comparison with ten baselines.