Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models

Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes and substructures in dynamic graphs. However, the representation learning problem for entire graphs in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised representation learning architecture for dynamic graphs, designed to learn both the topological and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs) and long short-term memory networks (LSTMs). The GGNN is able to learn the topology of the graph at each time step, while LSTMs are leveraged to propagate the temporal information among the time steps. Moreover, an encoder learns the temporal dynamics of an evolving graph and a decoder reconstructs the dynamics over the same period of time using the encoded representation provided by the encoder. We demonstrate that our approach is capable of learning the representation of a dynamic graph through time by applying the embeddings to dynamic graph classification using a real world dataset of animal behaviour. Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes and substructures in dynamic graphs. However, the representation learning problem for entire graphs in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised representation learning architecture for dynamic graphs, designed to learn both the topological and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs) and long short-term memory networks (LSTMs). The GGNN is able to learn the topology of the graph at each time step, while LSTMs are leveraged to propagate the temporal information among the time steps. Moreover, an encoder learns the temporal dynamics of an evolving graph and a decoder reconstructs the dynamics over the same period of time using the encoded representation provided by the encoder. We demonstrate that our approach is capable of learning the representation of a dynamic graph through time by applying the embeddings to dynamic graph classification using a real world dataset of animal behaviour.