CP-AGN: A causal prior-guided adaptive graph network for topology inference in non-cooperative networks

Accurate network topology inference is crucial for analyzing non-cooperative networks, where structural and routing information is unavailable. To address this challenge, we propose CP-AGN, a hybrid framework that combines a causal prior with a lightweight neural refinement module. Specifically, a multivariate Hawkes process is employed to capture potential causal relationships from network event sequences and construct a prior. Building upon this prior, a lightweight convolutional module performs edge pruning and confidence reweighting using pairwise correlation statistics. Extensive experiments on NS-3 simulated datasets under standard TCP/IP protocol assumptions demonstrate that CP-AGN effectively recovers hidden network structures while maintaining robust performance under noisy observations and packet loss.