A list of recent papers about Graph Neural Network methods applied in NLP areas.

This project focuses on predicting maternal-fetal health outcomes, specifically early detection of preeclampsia, by learning transferable representations from cfRNA and placental transcriptomic data.

Infra2Graph prototype for bridge impact assessment in city scale. The system integrates heterogeneous graph analysis and HGNN-based closure-impact prediction, with Exp-4-lite (kNN, no edge attributes, weak quantile-based weighting) adopted as the reference MVP configuration.

Time-Relaxed Directed GNN for Bitcoin Fraud Detection | 6 Novel Contributions | Production-Ready | E7-A3: 0.5846 PR-AUC (+4.1%) | E9 Fusion: +33.5% | Publication-Ready Research

ConHGNN-SUM: Contextualized Heterogeneous Graph Neural Networks for extractive document summarization. Published in IEEE AISP 2024. Revolutionizes summarization by modeling documents as dynamic graphs with semantic relationships between words and sentences.

PyTorch implementation of Heterogeneous Graph Neural Networks with attention aggregation for node classification on academic networks.

Implementation of a Heterogeneous Temporal GNN (HTGNN) for DeFi anomaly detection. Includes extensive baseline comparisons (RGCN, GAT, XGBoost), ablation studies, and a Neo4j-backed inference pipeline.

🕐 Uncover Bitcoin fraud with our zero-leakage temporal GNN, designed for effective detection through a systematic, experimental approach.