Forwarded from Graph Machine Learning
Simple scalable graph neural networks
Michael Bronstein continues a marathon of great blog posts on GML. In a new post he describes their recent work on scaling GNNs to large network. There is a good introduction to sampling-based methods (e.g. SAGE, GraphSAINT, ClusterGCN), which sample a subgraph of a large graph and then train GNN only on a subgraph.
Then, he describes that it can be beneficial just precompute r-hop matrices, A^r X, and use MLP on these features. This way, you use topology of your graph and you apply mini-batch training with MLP.
What's cool is that the algorithm is already available in pytorch-geometric as a transform, which is implemented via sparseTensor matrix multiplication.
Michael Bronstein continues a marathon of great blog posts on GML. In a new post he describes their recent work on scaling GNNs to large network. There is a good introduction to sampling-based methods (e.g. SAGE, GraphSAINT, ClusterGCN), which sample a subgraph of a large graph and then train GNN only on a subgraph.
Then, he describes that it can be beneficial just precompute r-hop matrices, A^r X, and use MLP on these features. This way, you use topology of your graph and you apply mini-batch training with MLP.
What's cool is that the algorithm is already available in pytorch-geometric as a transform, which is implemented via sparseTensor matrix multiplication.
Medium
Simple scalable graph neural networks
One of the practical challenges of graph neural networks in scalability to large graphs. We present a simple solution for scalable GNNs.
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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
#Baidu research proposed a structure-aware interactive graph neural network ( #SIGN ) to better learn representations of protein-ligand complexes, since drug discovery relies on the successful prediction of protein-ligand binding affinity.
Link: https://dl.acm.org/doi/10.1145/3447548.3467311
#biolearning #deeplearning
#Baidu research proposed a structure-aware interactive graph neural network ( #SIGN ) to better learn representations of protein-ligand complexes, since drug discovery relies on the successful prediction of protein-ligand binding affinity.
Link: https://dl.acm.org/doi/10.1145/3447548.3467311
#biolearning #deeplearning