MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning

Dynamic graph learning has attracted much attention in recent years due to the fact that most of the real-world graphs are dynamic and evolutionary. As a result, many dynamic learning methods have been proposed to cope with the changes of node states over time. Among these studies, a critical issue is how to update the representations of nodes when new temporal events are observed. In this paper, we provide a novel memory structure - Memory Map (MemMap) for this problem. MemMap is an adaptive and evolutionary latent memory space, where each cell corresponds to an evolving "topic" of the dynamic graph. Moreover, the representation of a node is generated from its semantically correlated memory cells, rather than linked neighbors of the node. We have conducted experiments on real-world datasets and compared our method with the SOTA ones. It can be concluded that: 1) By constructing an adaptive and evolving memory structure during the dynamic learning process, our method can capture the dynamic graph changes, and the learned MemMap is actually a compact evolving structure organized according to the latent "topics" of the graph nodes. 2) Our research suggests that it is a more effective and efficient way to generate node representations from a latent semantic space (like MemMap in our method) than from directly connected neighbors (like most of the previous graph learning methods). The reason is that the number of memory cells in latent space could be much smaller than the number of nodes in a real-world graph, and the representation learning process could well balance the global and local message passing by leveraging the semantic similarity of graph nodes via the correlated memory cells.