NodeSense2Vec: Spatiotemporal Context-Aware Network Embedding for Heterogeneous Urban Mobility Data

The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital.In this paper, we propose a spatiotemporal context-aware network embedding framework that jointly captures the spatial regularities between objects and the sequential transition patterns of human mobility. First, we model the heterogeneous urban mobility data collected from multiple sources as an SHN using a probabilistic weighted degree centrality measure. To learn the sequential transition patterns of human mobility in urban regions, we perform meta-path constrained random walks (MPCRWs) on the constructed SHN, which captures the proximities between multi-typed objects via their rich spatiotemporal links. By treating the generated meta-path instances as sentences, we capture multiple contrastive context senses associated with nodes in an SHN produced due to multiplex of spatial and temporal dependencies between objects in urban mobility data by performing spectral graph clustering. We then map the learned contrastive contextual node senses with respective meta-path instances. Finally, we learn latent embeddings of the mapped meta-path instances by using the word2vec model Skip-gram. We evaluate the performance of our proposed model on real-world application problems. Experimental results demonstrate the effectiveness of our model over state-of-the-art alternatives.