- Keywords: Unsupervised Learning, Graph Embedding, Community Detection, Mincut, Normalized cut, Deep Learning
- TL;DR: A neural network approach for unsupervised learning of node embeddings of a graph, while at the same time learning structural characteristics in terms of communities of nodes
- Abstract: We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph-structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide interesting insights into the graph structure, so that the separate node clustering step of existing methods is no longer needed. DMC learns both, node embeddings and communities, simultaneously by minimizing the mincut loss, which captures the number of connections between communities. Striving for high scalability, we also propose a training process for DMC based on minibatches. We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.