Technical Presentations Group 1: Algorithms, Foundations, Visualizations, and Engineering Applications Representation learning is a powerful framework for enabling the application of machine learning to complex data via vector representations. Here, we focus on representation learning for vertices of a graph using random walks. We introduce a framework for node embedding based on three dimensions: type of process, similarity metric, and embedding algorithm. Our framework not only covers many existing approaches but also motivates new ones. In particular, we apply it to produce new state-of-the-art results on link prediction.
