DeepWalk
DeepWalk is an unsupervised representation learning method for graphs. Introduced in 2014 by Bryan Perozzi, Rami Al-Rfou, and Steven Skiena, it learns low-dimensional vector representations of nodes that preserve network neighborhoods and structural similarities.
The core idea is to generate a large corpus of node sequences by performing truncated random walks
DeepWalk relies on uniform random walks to capture both local and global network structure. The learned embeddings
Applications include node classification, link prediction, clustering, and visualization. DeepWalk demonstrated that language-modeling techniques could be
Limitations include sensitivity to hyperparameters (walk length, number of walks, window size, embedding dimension), and its