Distributed Graph Embedding with Information-Oriented Random Walks

arXiv:2303.15702v2 Announce Type: replace-cross
Abstract: Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale …

abstract arxiv availability billion cs.dc cs.lg distributed edge embedding embeddings graph graphs importance information link prediction low machine machine learning maps nodes prediction random tasks twitter type vectors