Real-life graphs often exhibit intricate dynamics that evolve continuously over time. To effectively represent continuous-time dynamic graphs (CTDGs), various temporal graph neural networks (TGNNs) have been developed to model their dynamics and topological structures in Euclidean space. Despite their notable achievements, the performance of Euclidean-based TGNNs is limited and bounded by the representation capabilities of Euclidean geometry, particularly for complex graphs with hierarchical and power-law structures. This is because Euclidean space does not have enough room (its volume grows polynomially with respect to radius) to learn hierarchical structures that expand exponentially. As a result, this leads to high-distortion embeddings and suboptimal temporal graph representations. To break the limitations and enhance the representation capabilities of TGNNs, in this article, we propose a scalable and effective TGNN with hyperbolic geometries for CTDG representation (called ${\rm STGN}^h$ ). It captures evolving behaviors and stores hierarchical structures simultaneously by integrating a memory-based module and a structure-based module into a unified framework, which can scale to billion-scale graphs. Concretely, a simple hyperbolic update gate (HuG) is designed as the memory-based module to store temporal dynamics efficiently; for the structure-based module, we propose an effective hyperbolic temporal Transformer (HyT) model to capture complex graph structures and generate up-to-date node embeddings. Extensive experimental results on a variety of medium-scale and billion-scale graphs demonstrate the superiority of the proposed ${\rm STGN}^h$ for CTDG representation, as it significantly outperforms baselines in various downstream tasks.

Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.