The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

ABSTRACT: Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms h

SUBMITTER: Gu W 

PROVIDER: S-EPMC5640610 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature