Automated pronunciation assessment predicts pronunciation quality at the phone, word, and utterance levels. Recent joint models use both canonical and realized phone representations, but their mismatch is often encoded implicitly rather than preserved as a separate scoring path. This makes it difficult to trace how local phone errors affect word- and utterance-level scores. We propose HiGraph, which explicitly computes canonical-realized mismatch and propagates it through a hierarchical phone-to-word-to-utterance graph. This creates a structured discrepancy path from phone-level mismatch evidence to higher-level score representations. Under the same pipeline as our reproduced HMamba baseline, HiGraph improves word stress and utterance completeness on SpeechOcean762 (SO762). The learned word-level trace score also aligns with mispronunciation detection and diagnosis (MDD) ground truth. When words are ranked by this trace score, the top-ranked word contains a true mispronunciation in 81.5% of test utterances, and at least one of the top two words does so in 93.8% of test utterances. These results show that explicit discrepancy propagation can improve selected assessment dimensions while supporting feedback localization grounded in annotated mispronunciations.