  Graph neural networks have become an important tool for modeling structured
data. In many real-world systems, intricate hidden information may exist, e.g.,
heterogeneity in nodes/edges, static node/edge attributes, and spatiotemporal
node/edge features. However, most existing methods only take part of the
information into consideration. In this paper, we present the Co-evolved Meta
Graph Neural Network (CoMGNN), which applies meta graph attention to
heterogeneous graphs with co-evolution of node and edge states. We further
propose a spatiotemporal adaption of CoMGNN (ST-CoMGNN) for modeling
spatiotemporal patterns on nodes and edges. We conduct experiments on two
large-scale real-world datasets. Experimental results show that our models
significantly outperform the state-of-the-art methods, demonstrating the
effectiveness of encoding diverse information from different aspects.
