Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Understand

Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks.

• We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification.
• Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation.
• Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected.