I need to reproduce the key components of the Knowledge-Enhanced Hierarchical Heterogeneous Graph (KE-HHG) framework so we can study how well it predicts Big-Five personality traits from text. Everything will be done in Python, and the work is strictly for research—no production hardening, no paper writing, just faithful replication and light adaptation. Scope of work • Build a clean, reusable data-preprocessing pipeline for PAN 2015, Pandora and MyPersonality. • Develop the knowledge graph that the original framework relies on, using the sources and schema described in the paper (I will supply all references). • Implement and train the Character-Level Graph Network (CGN)-based classifier within the KE-HHG hierarchy, preferably in PyTorch Geometric or DGL. • Report standard personality-profiling metrics (accuracy, macro-F1, per-trait scores) so results can be compared directly with the published benchmarks. Acceptance criteria 1. Scripts run end-to-end on the three datasets with a single config switch. 2. Model performance reported in a concise README and reproducible via provided seeds. 3. Source code is well commented and ready for me to extend in follow-up experiments. I will break the job into clear milestones, provide any supplementary papers or annotation guidelines you need, and stay available for quick feedback. Looking forward to collaborating on a clean, verifiable replication.