Spatial transcriptomics (ST) helps us understand cell interactions, developmental processes, and disease progression within tissues by analyzing gene expression while preserving spatial information on tissue sections. However, the spatial distribution patterns of the same cell population may differ in different slice samples, and a single slice is difficult to adapt to spatial changes, making multi-slice integration methods a research hotspot in recent years. Traditional graph convolution relies on a fixed graph structure, whose adjacency relationships remain fixed during training. It cannot be adaptively updated according to feature changes and is difficult to reflect the spatial distribution differences between different slices. Dynamic graph convolutional neural networks (DGCNN), on the other hand, adaptively update based on node embeddings or features during training to capture complex spatial relationships. Therefore, we propose DGAE, a framework based on DGCNN for multi-slice ST data alignment and data enhancement. DGAE consists of two modules: DGAE_align and DGAE_recog. DGAE_align combines K-nearest neighbor (KNN) and r-radius to build a hybrid graph, and integrates the spatial information of different slices to achieve accurate spatial alignment. DGAE_recog aggregates the information of adjacent slices into the target slice for data enhancement, achieving effective transmission of information between different slices. Experimental results show that DGAE outperforms existing methods in multi-slice ST data alignment and also demonstrates superior performance in data enhancement tasks. In addition, DGAE has shown well adaptability and stability in spatial domain recognition, denoising and disease research, demonstrating the wide applicability and scalability of DGAE as a method for multi-slice ST data alignment and data enhancement.

Dynamic graphs are gaining importance in many real-world applications based on different graph queries. Due to the large volume and high dynamicity, people resort to compute approximations to answer graph queries. However, previous work primarily evaluates the relationship between nodes based on frequency, which is not sufficient in many cases. We observe that this relationship varying process is highly similar to the water cooling process in nature. Based on the observation, we formulate a new concept Intimacy with Newton's law of cooling, to illustrate the relationship between nodes. Currently, there is no prior algorithm tailored for Intimacy estimation. Because Intimacy varies in every time unit, the main challenge lies in how to record and update the Intimacy efficiently. In this paper, we propose a novel technique named Newton-Observe to address this challenge. The key idea of Newton-Observe is that we only decay the Intimacy when we observe/query it. Based on Newton-Observe, we develop a series of Newton sketches to answer three fundamental tasks of Intimacy in dynamic graphs. We theoretically prove that the Newton sketch can estimate the Intimacy within an additive constant error to the real Intimacy. Our experiments on real-world datasets and synthetic datasets show that Newton-Observe outperform the strawman solution by up to$570\times$smaller ARE and improve the throughput by up to$1.62\times$. All source codes are open sourced at Github anonymously.

Sequential recommendation systems seek to learn users' preferences to predict their next actions based on the items engaged recently. Static behavior of users requires a long time to form, but short-term interactions with items usually meet some actual needs in reality and are more variable. RNN-based models are always constrained by the strong order assumption and are hard to model the complex and changeable data flexibly. Most of the CNN-based models are limited to the fixed convolutional kernel. All these methods are suboptimal when modeling the dynamics of item-to-item transitions. It is difficult to describe the items with complex relations and extract the fine-grained user preferences from the interaction sequence. To address these issues, we propose a knowledge-aware sequential recommender with the attention-enhanced dynamic convolutional network (KAeDCN). Our model combines the dynamic convolutional network with attention mechanisms to capture changing dependencies in the sequence. Meanwhile, we enhance the representations of items with Knowledge Graph (KG) information through an information fusion module to capture the fine-grained user preferences. The experiments on four public datasets demonstrate that KAeDCN outperforms most of the state-of-the-art sequential recommenders. Furthermore, experimental results also prove that KAeDCN can enhance the representations of items effectively and improve the extractability of sequential dependencies.