Traffic flow prediction plays a pivotal role in intelligent transportation systems. While previous efforts have made significant advances in modeling spatio-temporal dependencies, the traffic data collected by sensors in real-world scenarios is inherently limited, and the scarcity of data impedes model optimization. Moreover, it is difficult for existing models to capture the dynamic properties caused by changes in traffic patterns because they often use shared parameterization to model the time correlations of all time periods. To address these challenges, we propose a Spatio-Temporal Contrastive Learning-based Adaptive Graph Augmentation (STCL-AGA) framework for traffic flow prediction, which captures and adapts to evolving traffic patterns by employing dynamic graph adjustments and flow data masking. Specifically, our approach leverages spatio-temporal correlations captured through convolution operations. It improves adaptability through adaptive graph augmentation, which dynamically adjusts the graph structure and incorporates nodes flow masking based on traffic conditions. This approach achieves accurate traffic predictions by effectively integrating temporal and spatial data transformations. Experiments on five benchmark datasets demonstrate that STCL-AGA consistently outperforms various state-of-the-art baselines.

Wei Zhu, Aaron Tian, Congrui Yin, Yuan Ni, Xiaoling Wang, Guotong Xie. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.