  Networks evolve continuously over time with the addition, deletion, and
changing of links and nodes. Such temporal networks (or edge streams) consist
of a sequence of timestamped edges and are seemingly ubiquitous. Despite the
importance of accurately modeling the temporal information, most embedding
methods ignore it entirely or approximate the temporal network using a sequence
of static snapshot graphs. In this work, we propose using the notion of
temporal walks for learning dynamic embeddings from temporal networks. Temporal
walks capture the temporally valid interactions (e.g., flow of information,
spread of disease) in the dynamic network in a lossless fashion. Based on the
notion of temporal walks, we describe a general class of embeddings called
continuous-time dynamic network embeddings (CTDNEs) that completely avoid the
issues and problems that arise when approximating the temporal network as a
sequence of static snapshot graphs. Unlike previous work, CTDNEs learn dynamic
node embeddings directly from the temporal network at the finest temporal
granularity and thus use only temporally valid information. As such CTDNEs
naturally support online learning of the node embeddings in a streaming
real-time fashion. Finally, the experiments demonstrate the effectiveness of
this class of embedding methods that leverage temporal walks as it achieves an
average gain in AUC of 11.9% across all methods and graphs.
