Dynamic link prediction (DLP) plays a critical role in understanding and forecasting evolving relationships in real-world systems across various domains. However, accurately predicting future links remains challenging, as existing methods often overlook the independent modeling of dynamic interactions within individual nodes and the fine-grained characterization of latent interactions across node sequences. To address these challenges, we propose FineFormer (Fine-grained Interactive Transformer), a novel framework that alternates between self-attention and cross-attention mechanisms, enhanced with layer-wise contrastive learning. This design enables FineFormer to uncover fine-grained temporal dependencies both within single node sequences and across different node sequences. Specifically, self-attention captures temporal-spatial dynamics within the interaction sequences of individual nodes, while cross-attention focuses on the complex interactions across the sequences of pairs of nodes. Additionally, by strategically applying layer-wise contrastive learning, FineFormer refines node representations and enhances the model's ability to distinguish between connected and unconnected node pairs during feature refinement. FineFormer is evaluated on five challenging and diverse real-world DLP datasets. Experimental results demonstrate that FineFormer consistently outperforms state-of-the-art baselines, particularly in capturing complex, fine-grained interactions in continuous-time dynamic networks.

Multiplex graph representation learning has attracted considerable attention due to its powerful capacity to depict multiple relation types between nodes. Previous methods generally learn representations of each relation-based subgraph and then aggregate them into final representations. Despite the enormous success, they commonly encounter two challenges: 1) the latent community structure is overlooked and 2) consistent and complementary information across relation types remains largely unexplored. To address these issues, we propose a clustering-enhanced multiplex graph contrastive representation learning model (CEMR). In CEMR, by formulating each relation type as a view, we propose a multiview graph clustering framework to discover the potential community structure, which promotes representations to incorporate global semantic correlations. Moreover, under the proposed multiview clustering framework, we develop cross-view contrastive learning and cross-view cosupervision modules to explore consistent and complementary information in different views, respectively. Specifically, the cross-view contrastive learning module equipped with a novel negative pairs selecting mechanism enables the view-specific representations to extract common knowledge across views. The cross-view cosupervision module exploits the high-confidence complementary information in one view to guide low-confidence clustering in other views by contrastive learning. Comprehensive experiments on four datasets confirm the superiority of our CEMR when compared to the state-of-the-art rivals.

Deep Video Quality Assessment (VQA) methods have shown impressive high-performance capabilities. Notably, no-reference (NR) VQA methods play a vital role in situations where obtaining reference videos is restricted or not feasible. Nevertheless, as more streaming videos are being created in ultra-high definition (e.g., 4K) to enrich viewers' experiences, the current deep VQA methods face unacceptable computational costs. Furthermore, the resizing, cropping, and local sampling techniques employed in these methods can compromise the details and content of original 4K videos, thereby negatively impacting quality assessment. In this paper, we propose a highly efficient and novel NR 4K VQA technology. Specifically, first, a novel data sampling and training strategy is proposed to tackle the problem of excessive resolution. This strategy allows the VQA Swin Transformer-based model to effectively train and make inferences using the full data of 4K videos on standard consumer-grade GPUs without compromising content or details. Second, a weighting and scoring scheme is developed to mimic the human subjective perception mode, which is achieved by considering the distinct impact of each sub-region within a 4K frame on the overall perception. Third, we incorporate the frequency domain information of video frames to better capture the details that affect video quality, consequently further improving the model's generalizability. To our knowledge, this is the first technology for the NR 4K VQA task. Thorough empirical studies demonstrate it not only significantly outperforms existing methods on a specialized 4K VQA dataset but also achieves state-of-the-art performance across multiple open-source NR video quality datasets.

Epilepsy, one of the world's most common neurological diseases, impacts over 1% of the global population. Accurate early prediction of epileptic seizure has a great influence on epileptic patients' lives and attracted extensive attention. However, existing methods do not fully consider the complexity of multi-channel electroencephalogram (EEG) signal, which is the most common measurement for epileptic seizures. In this letter, we propose a Dynamic Functional Connectivity neural Network (DynFCNet) for epileptic seizure prediction. The proposed DynFCNet can discover dynamic brain functional connections and generate dynamic functional connectivity graphs, as well as extract non-Euclidean features from multi-channel EEG signals by Graph Convolutional Network (GCN). To take Euclidean features into consideration, Convolutional Neural Network (CNN) as one of the branches is also employed. Further, we incorporated both intra-group loss and inter-group loss to enhance our DynFCNet. Extensive experiments were implemented on a public multi-channel EEG dataset (CHB-MIT). The results confirm that our proposal outperforms the other competitors

Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental assumption that the graph structure along which information propagates is perfect. However, the real-world graphs are inevitably incomplete or noisy, which violates the assumption, thus resulting in limited performance. Therefore, optimizing graph structure for GNNs is indispensable and important. Although current semi-supervised graph structure learning (GSL) methods have achieved a promising performance, the potential of labels and prior graph structure has not been fully exploited yet. Inspired by this, we examine GSL with dual reinforcement of label and prior structure in this article. Specifically, to enhance label utilization, we first propose to construct the prior label-constrained matrices to refine the graph structure by identifying label consistency. Second, to adequately leverage the prior structure to guide GSL, we develop spectral contrastive learning that extracts global properties embedded in the prior graph structure. Moreover, contrastive fusion with prior spatial structure is further adopted, which promotes the learned structure to integrate local spatial information from the prior graph. To extensively evaluate our proposal, we perform sufficient experiments on seven benchmark datasets, where experimental results confirm the effectiveness of our method and the rationality of the learned structure from various aspects.

Temporal link prediction on dynamic graphs has attracted considerable attention. Most methods focus on the graph at each timestamp and extract features for prediction. As graphs are directly compressed into feature matrices, the important latent information at each timestamp has not been well revealed. Eventually, the acquisition of dynamic evolution-related patterns is rendered inadequately. In this paper, inspired by the process of Super-Resolution (SR), a novel deep generative model SRG (Super Resolution Graph) is proposed. We innovatively introduce the concepts of the Low-Resolution (LR) graph, which is a single adjacent matrix at a timestamp, and the High-Resolution (HR) graph, which includes the link status of surrounding snapshots. Specifically, two major aspects are considered regarding the construction of the HR graph. For edges, we endeavor to obtain an extensive information transmission description that affects the current link status. For nodes, similar to the SR process, the neighbor relationship among nodes is maintained. In this form, we could predict the link status from a new perspective: Under the supervision of the graph moving average strategy, the conditional normalizing flow effectively realizes the transformation between LR and HR graphs. Extensive experiments on six real-world datasets from different applications demonstrate the effectiveness of our proposal.

Classification of nonprecipitation echoes of radar is an inevitable step in radar-based precipitation estimation. Among nonprecipitation echoes, clear air echoes are specifically difficult to distinguish for their similarity to precipitation echoes. This letter aims to conduct a pixelwise classification of clear air echoes for image sequences of the radar reflectivity. We propose the Static and Motion streams Network (SMNet) to simultaneously utilize the static and motion features. SMNet realizes capturing the spatiotemporal characteristics while maintaining the details of the current frame via a fusion structure and a novel training method. For feature fusion, the static and motion streams are concatenated. Then, for model training, we adopt a dynamic weight assignment strategy to further extract rich information. Finally, we validate our method on an S-band single-polarization radar in Beijing, China, from May to September 2018. The results demonstrate that the overall performance of SMNet is superior to other competitors.

Short-term quantitative precipitation forecasting (SQPF) using weather radar is an important but challenging problem as one must cope with inherent nonlinearity and spatiotemporal correlation in the data. In this article, we propose a novel deep learning model, named Inductive spatiotemporal Graph Convolutional Networks (InstGCNs), to overcome these issues in SQPF. The proposed InstGCN can learn a nonlinear mapping from historical radar reflectivity to future rainfall amounts and extract informative spatiotemporal representations simultaneously. Specifically, we first provide a formal definition for formulating the SQPF problem from a graph perspective. Then, based on radar reflectivity and rain gauge observation, we propose a novel graph construction approach that utilizes a special elliptic structure to model the spatial dependence of precipitation areas. In addition, a new Node level Differential Block (Node-DB) is introduced to tackle the nonstationary temporal dependence. To execute inductive graph learning for unseen nodes, we design to decompose a whole graph into subgraphs. We conduct extensive experiments on three real-world datasets in East China and a public weather radar dataset in the southeastern parts of France. The experimental results confirm the advantages of InstGCN compared with several state of the arts.

Accurate and high-resolution quantitative precipitation estimation (QPE) plays a crucial role in meteorology and hydrology. However, for acquiring a more accurate QPE, how to depict the complex nonlinear relationship between the radar reflectivity and the true rain rates, as well as adaptively explore the spatial dependencies of precipitation, remains extremely challenging. In this letter, we propose to incorporate the merits of graph convolutional regression networks (GCRNs) and address the aforementioned issues simultaneously in the GCRNs framework. Furthermore, in order to tolerate the variabilities of spatial correlation in the practical precipitation, we expand GCRNs with a multiconvolutional mechanism between the center node and its neighbor rain gauges. Thus, the ability to capture more complicated spatial characteristics of precipitation can be enhanced, and the phenomenon of overwhelming by the neighbor nodes can be released. Extensive experiments were implemented on 12 rainfall processes in Hangzhou, China, 2015. The experimental results confirm that our proposal consistently outperforms the state-of-the-art QPE models.

In meteorology, ensemble forecasting aims to post-process an ensemble of multiple members’ forecasts and make better weather predictions. While multiple individual forecasts are generated to represent the uncertain weather system, the performance of ensemble forecasting is unsatisfactory. In this p aper we conduct data analysis based on the expertise of human forecasters and introduce a machine learning method for ensemble forecasting. The proposed method, Label Distribution Learning with Climate Probability (LDLCP), can improve the accuracy of both deterministic forecasting and probabilistic forecasting. The LDLCP method utilizes the relevant variables of previous forecasts to construct the feature matrix and applies label distribution learning (LDL) to adjust the probability distribution of ensemble forecast. Our proposal is novel in its specialized target function and appropriate conditional probability function for the ensemble forecasting task, which can optimize the forecasts to be consistent with local climate. Experimental testing is performed on both artificial data and the data set for ensemble forecasting of precipitation in East China from August to November, 2017. Experimental results show that, compared with a baseline method and two state-of-the-art machine learning methods, LDLCP shows significantly better performance on measures of RMSE and average continuous ranked probability score.

We introduce a (semi-)automatic OCR post-processing system that utilizes web-scale linguistic corpora in providing high-quality correction. This paper is a comprehensive system overview with the focus on the computational procedures, applied linguistic analysis, and processing optimization.