Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.
One application is predicting protein function from tissue-specific networks. I didn't check their reference to see where those networks come from.
PPI networks are one of the standard benchmarks in learning embeddings for large graphs. it could probably make up another mini section, the topic is very exciting :)
https://arxiv.org/abs/1706.02216
One application is predicting protein function from tissue-specific networks. I didn't check their reference to see where those networks come from.