I will build graph neural networks in tensorflow and pytorch

Graph Neural Networks have moved beyond basic classification. In 2026, the real value is in graph transformers (better at long-range dependencies) and GNN-LLM hybrid systems combining graph structure with language reasoning for GraphRAG, knowledge graph reasoning, and relational data at scale. I build both.

What I offer:

• Custom GNN & graph transformer models node/link/graph classification, using GCN/GAT/GraphSAGE or newer transformer architectures, matched to your data
• GNN-LLM hybrids GraphRAG pipelines and structure-aware retrieval
• Generative GNNs molecule generation, network synthesis, structured data generation
• Data preprocessing, graph construction, and tuning included in delivery

Real impact: companies embedding graph structure into recommendation and retrieval systems have reported major accuracy gains and multi-x performance improvements over flat, structure-blind approaches.

Why me: production ML background I evaluate whether your problem needs GNNs, graph transformers, or a hybrid before building anything.

Message me your data and goal, and I'll tell you what fits.