Tutorials for Machine Learning on Graphs

[IJCNN 2021] Unified Spatio-Temporal modeling for traffic forecasting using Graph Convolutional Network

Research Project I completed under Dr Vinti Agrawal at BITS Pilani.

Data and code for Salesforce Research paper, GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning - https://arxiv.org/abs/2012.03900 . The paper provides methods for constraint graph augmentation and optimal facility placement problems

Pure Go machine learning framework. Train, run, and serve ML models with go build. Zero CGo.

A Task-Oriented graph SAmpler for HGNN

CLEVR graph: A dataset for graph based reasoning

End-to-end graph AML investigator with explainable scoring, what-if analysis, and operator-facing UI.

Machine learning on graphs

Production-grade fraud detection pipeline with entity-level behavioral feature engineering, velocity anomaly detection, graph-based risk signals, and real-time scoring API. Built with XGBoost, PyTorch, FastAPI, and Docker.

Graph-RAG for Customer Journey Intelligence using NetworkX + LLM. Path-aware retrieval outperforming vector RAG on temporal queries, cohort comparison with real statistics, 5 pre-built analytics queries, and fully dockerized FastAPI/Streamlit architecture deployed on HuggingFace Spaces.

Conditional VGAE for generating synthetic temporal contact networks from node metadata

Compare LLM text embeddings with structure-aware Graph AI (GNN link prediction) on any dataset with nodes, text, and edges.

Graph-native fraud ring detection with hybrid ML/DL scoring, explainability, and production observability.

A deep learning architecture combining spectral graph neural networks with curriculum learning for HOMO-LUMO gap prediction on PCQM4Mv2. Features a dual-view architecture with Chebyshev polynomial-based spectral convolutions and complexity-driven training schedules.

Self-Supervised Similarity Learning of Floor Layouts

LaTeX research writing repo with structured build workflow and supporting artifacts.

Use NetworkViz to visualize IP Traffic flow as Graph ML problem

Production-grade AML intelligence platform combining Graph ML, XGBoost, and LLMs with explainability and local deployment.

Graph representation learning — reproducing and analyzing core methods for academic study