What is the difference between the basic Graph Convolutional Neural Networks and GraphSage?
Which of the methods is more suited to unsupervised learning and in that case how is the loss function defined?
Please share the base papers for both the methods.
Graph Convolutional Networks are inherently transductive i.e they can only generate embeddings for the nodes present in the fixed graph during the training. This implies that, if in the future the graph evolves and new nodes (unseen during the training) make their way into the graph then we need to retrain the whole graph in order to compute the embeddings for the new node. This limitation makes the transductive approaches inefficient to get applied on the ever-evolving graphs (like social networks, protein-protein networks, etc) because of their inability to generalize on unseen nodes.
On the other hand, the GraphSage algorithm exploits the rich node features and the topological structure of each node’s neighborhood simultaneously to generate representations for new nodes without retraining efficiently. In addition to this GraphSage performs neighborhood sampling which provides the GraphSage algorithm its unique ability to scale up to billions of nodes in the graph
To find more detail one can follow this blogpost https://sachinsharma9780.medium.com/a-comprehensive-case-study-of-graphsage-algorithm-with-hands-on-experience-using-pytorchgeometric-6fc631ab1067
