The pervasive issue of missing data in statistical analysis and data science significantly impacts the integrity and accuracy of Time-Series Cross-Sectional (TSCS) datasets, extensively used in domains like health sciences and sensor applications. Traditional imputation methods are often inadequate for these datasets as they fail to address the complexities arising from cross-sectional and temporal data gaps. This paper introduces BiTSNet (Bipartite Time Series Network for Data Imputation), a novel approach designed to tackle the unique challenges of TSCS datasets by leveraging a dual representation of data. BiTSNet models cross-sectional time series data as sequences of bipartite graphs, where each graph represents a specific time step, and feature values are depicted as edge weights. Thus, missing values are interpreted as missing edge feature values, allowing BiTSNet to comprehensively capture spatial relationships within each time step and temporal relationships across steps. The framework uses Graph Neural Networks (GNNs) to process spatial dependencies within these bipartite graph representations and employs Recurrent Neural Networks (RNNs) to handle temporal dependencies. This integration enables BiTSNet to learn the intricate intra-time step relationships and the inter-time step dynamics effectively. Our approach not only preserves the longitudinal and cross-sectional integrity of the data but also ensures the production of valid and insightful conclusions from the enriched dataset. We evaluated BiTSNet on several datasets and compared it against the state-of-the-art approaches with encouraging results.