The widespread adoption of graph neural networks (GNNs) has brought increased attention to fairness issues related to sensitive attributes, such as gender and race, in practical scenarios. However, this concern remains largely unexplored in the context of graph clustering. Conventional fair graph clustering methods primarily depend on spectral clustering approaches. Meanwhile, we argue that existing graph learning works mainly focus on a single type of fairness, whereas graph clustering should achieve group equality-informed individual fairness. In this paper, we introduce for the first time a fairness-aware framework termed FairGC for deep graph clustering, which integrates the dual objectives of individual and group fairness while maintaining accurate clustering results. Specifically, we construct two views with distinct semantics using Siamese encoders. Then, we apply multi-step random walks on view-specific affinity graphs to capture high-order affinities of node pairs, thereby reformulating the contrastive learning with a focus on individual similarity. Besides, we utilize adversarial learning by making node representations independent of the estimated sensitive attributes to further eliminate group biases of clustering results. Extensive experiments on four benchmarks demonstrate the effectiveness and superiority of our proposed framework FairGC.

Deep neural networks (DNNs) have achieved extensive progress in time series learning. However, they could suffer from performance degradation when it comes to label noise in the real world. Towards this end, this paper studies an underexplored yet realistic problem of noise-resistant time series learning and proposes a novel data-centric approach named Dual Data-centric Separation with Circular Mixup (DREAM) for this problem. The core of our DREAM is to explore and exploit the noisy data from dual data-centric views for reduced overfitting. On the one hand, we assume that samples with similar features share similar labels and infer the pseudo label of each sample using its affinity graph to capture the corresponding pseudo margin. On the other hand, we monitor the optimization status by simulating the mislabeled data to generate flexible criteria for accurate separation of clean and noisy samples. In addition, we leverage circular Mixup to interpolate between clean and noisy samples in the embedding space. These mixed samples are incorporated into a discrepancy-aware consistency learning framework to ensure robust time series representations of all the separated samples. Experimental results on a wide range of publicly accessible datasets reveal the effectiveness of our DREAM.

Molecular property prediction plays a crucial role in drug discovery, but is always challenged by the limited number of effective labels. Compared with existing methods, we argue that the auxiliary properties of the molecule and the heterogeneous structure of different property prediction tasks have always been ignored. In this article, we propose a novel framework termed Meta-DREAM for few-shot molecular property prediction, which tailors to learning the transferable knowledge within different clusters of tasks. Specifically, we first construct a heterogeneous molecule relation graph (HMRG) with molecule-property and molecule-molecule relations to utilize many-to-many correlations between properties and molecules. The meta-learning episode can, then, be reformulated as a subgraph of HMRG. Next, we propose a disentangled graph encoder to explicitly discriminate the underlying factors of the task. In addition, we introduce a soft clustering module to group each factorized task representation into appropriate clusters and preserve knowledge generalization within a cluster and customization among clusters. In this way, each disentangled factor serves as a cluster-aware parameter gate for the task-specific meta-learner. Extensive experiments on five commonly used molecular datasets show that Meta-DREAM consistently outperforms existing state-of-the-art methods and verifies the effectiveness of each module.

With the recent improvement of deep learning (DL) techniques and computer hardware capabilities, neural networks are widely used to monitor massive sensor data and detect earthquakes in them. This makes designing fast, accurate, and generalized DL models necessary for an active field of research for automatic seismic phase picking. A seismic phase picking network called MFFnet is proposed to fuse power spectral density (PSD), expert knowledge, spectrograms, recurrence plots (RPs), and Gramian angle fields. The network uses fast Fourier convolution (FFC) on 2-D representations to extract more interpretable features. Considering the high proportion of noisy signals in field applications, MFFnet uses focal loss (FL) as the loss function to improve network accuracy. Experimental results show that MFFnet achieves precision, recall, and accuracy with 0.96, 0.98, and 0.98, respectively, in seismic phase detection tasks. Shapley value is used to evaluate the relationship between features and network predictions. Compared with other DL networks, the feature extraction approach used in this letter is more explanatory and provides greater confidence in the results.

Rapid and accurate classification of earthquake (eq) events is a serious challenge in seismology and disaster mitigation. Problems, such as data imbalance, model interpretability, and model generalization, limit the application of artificial intelligence methods in this research area. This article introduces a real-time two-stage diffusion eq event classification (DiffEEC) model based on the diffusion probability model (DPM). DiffEEC uses a two-stage classification approach and combines DPM and knowledge distillation (KD) techniques. DiffEEC focuses on seismic phase-related features and source mechanism-related features by combining time-series features extracted by convolutional layers, DPM output, and InSAR data features to better extract core seismic data information and reduce reliance on manual feature design. DiffEEC uses focal loss to solve the data imbalance problem. Thus, DiffEEC can address data scarcity and imbalance, feature acquisition and selection, variability and complexity of seismic event processing, and model generalization through the mechanism. Experiments show that DiffEEC performs better in eq event classification (EC).

Rapid and accurate seismic source characterization significantly influences the performance of Earthquake Early Warning (EEW) systems. However, the complexity of the seismic source modeling and the error accumulation during continuous characterization make it difficult to accurately characterize various source parameters. Furthermore, current artificial intelligence methods focus on a single task, lacking inter-task fusion and guidance from specialized knowledge. In this study, we propose a novel Deep Learning (DL) algorithm (SeisParaNet) to estimate P-wave arrival time, source location, and magnitude simultaneously based on a multi-task framework. To exploit seismological knowledge and attenuate the strong inter-task dependencies, this study incorporates arrival time differences information into the analysis of source localization parameters by using the attention mechanism and incorporates source location features into estimating local magnitude (ML). In addition, SeisParaNet uses a probability-based Self-Attention mechanism (Prob-Attention) to extract temporal information from waveforms. Experimental results demonstrate that, following a limited number of trainings on the STanford EArthquake Dataset (STEAD), SeisParaNet exhibits the capability to capture complex seismic patterns and rapidly characterize seismic sources. Furthermore, the introduction of Prob-Attention reduces computational complexity by 67%, validating the potential of SeisParaNet in EEW applications.

Peak ground acceleration (PGA) is a critical parameter in the postearthquake intensity snapshot. In this article, we propose a Bagged Tree for Rapid PGA Prediction (BTRPP) based on velocity waveforms recorded at stations around the epicenter, as well as a feature pruning algorithm based on BTRPP to predict PGA and analyze the relationship between PGA and manually predefined features. The research not only aims to discover the best model for forecasting PGA but also examines the impact of features using the interpretability of some models. As inputs to the model, BTRPP uses and mixes features in the time and frequency domains. Bayesian optimization (BO) is used to find the optimum hyperparameters of BTRPP. The results demonstrate that, using the data within 5 s after the first arrival of the P-wave, the BTRPP with BO can reduce the RMSE to 0.2076 and enhance the R-square to 0.91. It is, finally, found that the PGA is most closely related to the average positive amplitude, average negative amplitude, and spectral slope through the feature pruning algorithm in this article, which indicates that the results are plausible.