Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments demonstrate that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in terms of imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.

Influence Maximization (IM), which aims to identify the most influential k nodes in a network, is fundamental to numerous applications, including viral marketing and recommendation systems. This topic has garnered significant scholarly attention. However, most existing research addresses the IM problem in static networks, neglecting the dynamic and continually evolving nature of social networks. In this paper, we introduce a novel problem: Influential Nodes Tracking in Future Networks (INTFN). The INTFN problem aims to quickly find the most influential k nodes in networks over upcoming time intervals. We formally define the INTFN problem and prove its NP-hardness. To address this challenge, we propose a comprehensive solution that predicts the future structure of social networks using a carefully selected link prediction technique. Subsequently, we identify the most influential k nodes in these future networks by employing classic IM algorithms. Additionally, we design a dictionary structure and propose the Compressed Subgraphs based Influential Nodes Tracking (CSINT) algorithm to enhance the efficiency of our solution. Extensive experiments on four real-world datasets demonstrate the effectiveness and efficiency of the proposed CSINT algorithm.