L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders

For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are obtained through minimising a loss function; these embeddings are used with a decoder for downstream tasks such as node classification and edge prediction. While GAEs tend to be fairly accurate, they suffer from scalability issues. For improved speed, a Local2Global approach, which combines graph patch embeddings based on eigenvector synchronisation, was …

autoencoder autoencoders classification cs.ai cs.lg cs.si decoder edge embedding embeddings function global graph graph representation loss low network networks node popular representation representation learning scalable stat.ml tasks through tool world