  Graphs are essential representations of many real-world data such as social
networks. Recent years have witnessed the increasing efforts made to extend the
neural network models to graph-structured data. These methods, which are
usually known as the graph neural networks, have been applied to advance many
graphs related tasks such as reasoning dynamics of the physical system, graph
classification, and node classification. Most of the existing graph neural
network models have been designed for static graphs, while many real-world
graphs are inherently dynamic. For example, social networks are naturally
evolving as new users joining and new relations being created. Current graph
neural network models cannot utilize the dynamic information in dynamic graphs.
However, the dynamic information has been proven to enhance the performance of
many graph analytic tasks such as community detection and link prediction.
Hence, it is necessary to design dedicated graph neural networks for dynamic
graphs. In this paper, we propose DGNN, a new {\bf D}ynamic {\bf G}raph {\bf
N}eural {\bf N}etwork model, which can model the dynamic information as the
graph evolving. In particular, the proposed framework can keep updating node
information by capturing the sequential information of edges (interactions),
the time intervals between edges and information propagation coherently.
Experimental results on various dynamic graphs demonstrate the effectiveness of
the proposed framework.
