A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework

Spatiotemporal data forecasting is a fundamental task in the field of graph data mining. Typical spatiotemporal data prediction methods usually capture spatial dependencies by directly aggregating features of local neighboring vertices in a fixed graph. However, this kind of aggregators can only capture localized correlations between vertices, and while been stacked for larger receptive field, they fall into the dilemma of over-smoothing. Additional, in temporal perspective, traditional methods focus on fixed graphs, while the correlations among vertexes can be dynamic. And time series components integrated strategies in traditional spatiotemporal learning methods can hardly handle frequently and drastically changed sequences. To overcome those limitations of existing works, in this paper, we propose a novel multi-graph based dynamic learning framework. First, a novel Dynamic Neighbor Search (DNS) mechanism is introduced to model global dynamic correlations between vertices by constructing a feature graph (FG), where the adjacency matrix is dynamically determined by DNS. Then we further alleviate the over-smoothing issue with our newly designed Adaptive Heterogeneous Representation (AHR) module. Both FG and origin graph (OG) are fed into the AHR modules and fused in our proposed Multi-graph Fusion block. Additionally, we design a Differential Vertex Representation (DVR) module which takes advantage of differential information to model temporal trends. Extensive experiments illustrate the superior forecasting performances of our proposed multi-graph based dynamic learning framework on six real-world spatiotemporal datasets from different cities and domains, and this corroborates the solid effectiveness of our proposed framework and its superior generalization ability.