Bi-Modal Learning for Networked Time Series

Understanding human mobility patterns is a complex challenge that requires modeling both node-oriented time series (e.g., population) and edge-oriented time series (e.g., population flows) within graph topologies across time.While previous methods have focused on either node-oriented time series or interactions, the synergistic integration of these two modalities has proven difficult to achieve.In this paper, we propose BINTS (BI-modal learning for Networked Time Series), a novel bi-modal learning framework that employs soft contrastive learning along the temporal axis.BINTS captures modality similarities and temporal patterns by simultaneously learning from evolving node-oriented time series and interactions, solving the limitations of single-modality approaches.To evaluate our method, we curate comprehensive multi-modal human mobility datasets spanning diverse locations and times.Our experimental results demonstrate that BINTS significantly outperforms existing forecasting models by capturing synergies across different data modalities.Overall, we establish BINTS as a powerful technique for holistically understanding and forecasting complex mobility dynamics.For reproducibility, the source code of our framework is available at https://github.com/kaist-dmlab/BINTS.