AGGCN applies graph convolutional layers over dependency parse trees to capture syntactic structure for sentence-level relation extraction. The model uses attention mechanisms to guide the graph convolutions, learning which dependency paths are most relevant for predicting relations between entities. It is trained and evaluated on standard NLP benchmarks including TACRED, SemEval Task 8, and PubMed, with GloVe word embeddings as initial features.