Thailand GNN learning to tile with self supervised graph neural network many problems in computer graphics face combinatorial optimizations which are typically solved by approximation algorithms are heuristic search methods in this work we explore whether a learning-based approach can solve a classical combinatorial geometric problem tiling our problem specifically we focus on tiling the interior of an arbitrary 2d shape using a given tile set while avoiding holes in tile overlaps our trained network can help produce