Graph Neural Networks (GNNs) are a class of deep learning models specifically designed to operate on graph-structured data. Unlike traditional neural networks that work on Euclidean data (images, sequences), GNNs can learn from nodes, edges, and entire graph structures making them ideal for modeling relationships, interactions, and complex dependencies.
Graphs offer a powerful way to represent abstract relationships and interconnected systems. They serve as a natural structure for:
- Social networks
- Molecular structures & protein interactions
- Knowledge graphs
- Communication networks
- Recommendation systems
Graphs allow us to model entities and relationships intuitively, enabling flexible and expressive learning frameworks.
Traditional graph algorithms (PageRank, BFS, community detection, etc.) often require:
- Extensive domain knowledge
- Manual feature engineering
- Limited generalization to graph-level tasks
They cannot natively support:
❌ Graph-level classification ❌ End-to-end feature learning ❌ Adaptive representation learning
This is where GNNs excel.
GNNs make it possible to perform:
(e.g., node classification, fraud detection)
(e.g., link prediction, recommendation systems)
(e.g., molecule classification, program analysis)
They enable end-to-end learning, automatically extracting topological features and relational patterns without manual feature engineering.
Graph Neural Networks are still rapidly evolving and represent one of the most exciting frontiers in deep learning research. Their ability to generalize to any problem that can be represented as a graph makes them extremely powerful.
This project aims to:
- Explore modern GNN architectures
- Understand how graph representations enhance learning
- Apply GNNs to real-world tasks across NLP, CV, and structured data
- Build an industrial-grade GNN pipeline for research & deployment
Although text is fundamentally sequential, GNNs approach NLP from a relational perspective. Instead of relying on RNNs/LSTMs, GNNs model:
- Word co-occurrence graphs
- Dependency parse trees
- Document relationship graphs
This enables richer context understanding and improved classification performance.
CNNs excel at object detection, but they lack the ability to capture relationships between objects.
GNNs complement CNNs by:
- Building graphs over detected objects
- Learning inter-object relationships
- Enhancing scene understanding
- Improving tasks like VQA, action recognition, and object grouping
This combination unlocks context-aware vision models.
Graph Neural Networks bridge the gap between structured relationships and deep learning, providing a unified framework for complex real-world data. With applications across NLP, CV, biology, and networks, GNNs continue to be one of the most influential innovations in modern AI research.
This repository presents an industry‑grade framework that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) for hybrid reasoning. The project demonstrates how graph‑structured knowledge (e.g., social networks, code graphs, bioinformatics) can be injected into LLMs via graph‑to‑text conversion and instruction tuning, enabling models to reason jointly over structured and unstructured data.
The pipeline combines:
- Graph ML: Node embeddings and classification via a lightweight GraphSAGE‑style GNN.
- LLM fine‑tuning: HuggingFace Transformers with PEFT/LoRA adapters for efficient adaptation.
- Hyperparameter optimization: Automated search with Optuna.
- Agentic AI workflows: Tool‑calling orchestration conditioned on graph context.
- Reinforcement learning evaluation: Reward shaping for structural fidelity.
Enterprises increasingly need AI systems that can reason over structured data (graphs, knowledge bases) and unstructured text (documents, conversations).
This project demonstrates a scalable hybrid workflow that bridges these modalities, showcasing skills in:
- Quantum‑inspired graph learning,
- LLM infrastructure engineering,
- Agentic orchestration,
- Reinforcement learning integration.
- Synthetic Graph Generation: Social‑like Barabási–Albert graphs with node features and labels.
- GNN Training: GraphSAGE‑style aggregator producing embeddings and node classifications.
- Graph‑to‑Text Conversion: Embeddings and topology converted into natural language instructions.
- LLM Fine‑Tuning: GPT‑style causal LM fine‑tuned with LoRA adapters for efficiency.
- Hyperparameter Optimization: Optuna search over learning rate, adapter rank, and block size.
- Agentic Hybrid Reasoning: Routing tasks (summarize, classify, extract) based on graph properties, refined by LLM outputs.
- RL‑Style Reward Shaping: Evaluating structural fidelity of LLM outputs with heuristic rewards.
- Artifact Logging: Configs, metrics, and hybrid outputs serialized for reproducibility.
- Generate synthetic graphs with node features and labels.
- Train GNN to produce embeddings and classify nodes.
- Convert embeddings + topology into text instructions.
- Fine‑tune LLM on these instruction‑response pairs using LoRA adapters.
- Optimize hyperparameters with Optuna.
- Run agentic workflows: route tasks based on graph context, refine with LLM.
- Evaluate with RL‑style rewards for structural fidelity.
- Log artifacts for reproducibility and reporting.
- GNN achieves meaningful node classification accuracy on synthetic graphs.
- LLM learns to generate summaries, classifications, and property extractions conditioned on graph context.
- Optuna HPO improves convergence and efficiency.
- Agentic orchestration demonstrates hybrid reasoning: combining tool outputs with LLM refinement.
- RL metrics quantify structural fidelity of outputs.
- Extend to PyTorch Geometric/DGL for advanced GNN architectures (GCN, GAT, Graph Transformers).
- Apply to real datasets (social networks, code graphs, bioinformatics).
- Integrate tensor networks or quantum‑inspired embeddings for richer representations.
- Explore RLHF (PPO/DPO) for fine‑grained reward optimization.
- Scale to multi‑GPU/TPU clusters with Accelerate/DeepSpeed.
This project demonstrates:
- Systems Integration: Bridging GNNs and LLMs in a unified pipeline.
- Infrastructure Engineering: Efficient fine‑tuning with LoRA, adaptive workload allocation, hyperparameter optimization.
- Hybrid AI Workflows: Agentic orchestration and RL evaluation.
- Reproducibility & Rigor: Configs, metrics, and artifacts logged for transparency.
- Enterprise Relevance: Hybrid reasoning is critical for applications in code intelligence, social analytics, and enterprise knowledge graphs.