This is a PyTorch implementation of the GeniePath model in GeniePath: Graph Neural Networks with Adaptive Receptive Paths
GeniePath, a scalable approach for learning adap- tive receptive fields of neural networks defined on permuta- tion invariant graph data. In GeniePath, we propose an adap- tive path layer consists of two complementary functions de- signed for breadth and depth exploration respectively, where the former learns the importance of different sized neighbor- hoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive ex- periments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs
pip install -r requirements.txt
# pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simplein ppi_geniepath.py
# from model_geniepath import GeniePath as Net
from model_geniepath import GeniePathLazy as Netpython ppi_geniepath.py- Finish the rough implementation, f1_score: 0.9709 for GeniePath, 0.9762 for GeniePathLazy (dim = 256, lstm_hidden = 256).
- Tune the model
- Contribute to pytorch_geometric/examples/geniepath.py