  This tutorial covers a few recent papers in the field of network embedding.
Network embedding is a collective term for techniques for mapping graph nodes
to vectors of real numbers in a multidimensional space. To be useful, a good
embedding should preserve the structure of the graph. The vectors can then be
used as input to various network and graph analysis tasks, such as link
prediction. The papers discussed develop methods for the online learning of
such embeddings, and include DeepWalk, LINE, node2vec, struc2vec and
megapath2vec. These new methods and developments in online learning of network
embeddings have major applications for the analysis of graphs and networks,
including online social networks.
