GRPCI: Harnessing Temporal-Spatial Dynamics for Graph Representation Learning

Temporal interactions form the crux of numerous real-world scenarios, thus necessitating effective modeling in temporal graph representation learning. Despite extensive research within this domain, we identify a significant oversight in current methodologies: the temporal-spatial dynamics in graphs, encompassing both structural and temporal coherence, remain largely unaddressed. In an effort to bridge this research gap, we present a novel framework termed Graph Representation learning enhanced by Periodic and Community Interactions (GRPCI). GRPCI consists of two primary mechanisms devised explicitly to tackle the aforementioned challenge. Firstly, to utilize latent temporal dynamics, we propose a novel periodicity-based neighborhood aggregation mechanism that underscores neighbors engaged in a periodic interaction pattern. This mechanism seamlessly integrates the element of periodicity into the model. Secondly, to exploit structural dynamics, we design a novel contrastive-based local community representation learning mechanism. This mechanism features a heuristic dynamic contrastive pair sampling strategy aimed at enhancing the modeling of the latent distribution of local communities within the graphs. Through the incorporation of these two mechanisms, GRPCI markedly augments the performance of graph networks. Empirical evaluations, conducted via a temporal link prediction task across five real-life datasets, attest to the superior performance of GRPCI in comparison to existing state-of-the-art methodologies. The results of this study validate the efficacy of GRPCI, thereby establishing a new benchmark for future research in the field of temporal graph representation learning. Our findings underscore the importance of considering both temporal and structural consistency in temporal graph learning, and advocate for further exploration of this paradigm.