Random Walk in Node Embeddings (DeepWalk, node2vec, LINE, and GraphSAGE)

Instead of using traditional machine learning classification tasks, we can consider using graph neural network (GNN) to perform node classification problems. By providing an explicit link of nodes, this classification problem is no longer classified as an independent problem but leveraging graph structures such as the degree of nodes. The usefulness of graph properties assumes that individual nodes are correlated with other similar nodes. Typically example is a social media network. Imagine how Facebook connects you and somebody else based on what post you like, where you check-in etc. A graph is capable to represent this kind of relationship and we can leverage it to train GNN.