In the global pursuit of carbon neutrality, the manufacturing industry is under increasing pressure to reduce energy waste. Excess consumption not only depletes resources but also hinders sustainable development. Accurate energy consumption prediction is therefore essential for scientific production scheduling and resource allocation, enabling loss reduction, efficiency improvement, and environmental performance enhancement. However, the complexity of modern manufacturing environments results in energy consumption data that is high-dimensional, noisy, and strongly spatiotemporal, which poses challenges to traditional prediction methods. To address these issues, this paper constructs an energy consumption behavior model considering key factors such as equipment status, processing techniques, and environmental conditions. A comprehensive feature analysis and data preprocessing are carried out to identify the key factors influencing consumption. Based on this, an optimization model is proposed that integrates an improved Variational Autoencoder (VAE) with an enhanced Graph Attention Network (GAT). VAE extracts compact latent representations from high-dimensional noisy inputs, suppressing redundancy while preserving essential patterns. GAT then captures complex spatiotemporal dependencies among energy-related features, thereby revealing intrinsic consumption dynamics. Experimental evaluations on both public and real-world datasets demonstrate that the proposed VAE-GAT model achieves superior prediction accuracy and generalization compared with other deep learning baselines. This approach provides a reliable foundation for energy management and contributes to advancing green intelligent manufacturing.

Hypergraphs, by extending traditional graphs with hyperedges, enable the modeling and prediction of complex higher-order interactions that go beyond simple pairwise interactions. Hyperedge prediction, an evolution of link prediction, aims to identify potential higher-order interactions—such as those in social media group chats—by recognizing and predicting hyperedges. Recently, hypergraph neural networks (HGNNs) have advanced hyperedge prediction by structuring higher-order interactions into a hypergraph, enabling effective capture of higher-order relations through information propagation across the hypergraph. However, existing methods primarily focus on developing complex HGNNs, underestimating the inherent unreliability of the underlying hypergraph due to incompleteness and noise, leading to suboptimal and fragile performance. In this article, we propose Multi-HyperLinker, a novel multi-view hypergraph learning framework that leverages the consistency and invariance across multiple views to capture reliable higher-order interaction patterns from historical observational data for robust hyperedge prediction. Specifically, to facilitate effective information propagation on incomplete hypergraphs, Multi-HyperLinker first synthesizes a tightly structured hypergraph and designs a consistency-guided dual-view learning strategy. To capture reliable higher-order interaction patterns on noisy hypergraphs, Multi-HyperLinker augments the hypergraphs by perturbing hyperedges to simulate variations and noise, and introduces an invariant learning strategy. Extensive experiments conducted on four real-world datasets demonstrate the superiority of Multi-HyperLinker, achieving performance improvements of up to 19.80% in hit rate compared to existing HGNN-based methods. Additionally, it exhibits enhanced robustness on incomplete and noisy hypergraphs.

As a challenging sub-task of event argument extraction, implicit event argument extraction seeks to identify document-level arguments that play direct or implicit roles in a given event. Prior work mainly focuses on capturing direct relations between arguments and the event trigger; however, the lack of reasoning ability imposes limitations to the extraction of implicit arguments. In this work, we propose anArgument-argumentRelation-enhancedEventArgument extraction (AREA) learning framework to tackle this issue through reasoning in event frame-level scope. The proposed method leverages related arguments of the expected one as clues, and utilizes such argument-argument dependencies to guide the reasoning process. To bridge the distribution gap between oracle knowledge used in the training phase and the imperfect related arguments in the test stage, we introduce a conventional knowledge distillation strategy to drive a final model that can work without extra inputs by mimicking the behaviour of a well-informed teacher model. In addition, considering that conventional knowledge distillation methods transfer knowledge individually, we integrate it with a novel relational knowledge distillation mechanism to explicitly capture the structural mutual argument-argument relation. Moreover, since the training process is not compatible with the real situation, a curriculum learning method is further introduced to make the training process smoother. Experimental results demonstrate that the learning framework obtains state-of-the-art performance on the RAMS and Wikievents datasets. Ablation study and further discussion also show it could handle long-range dependency and implicit argument problems effectively.

Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complete set of those with constraints still poses a huge challenge to the existing algorithms mainly due to the inherent bottleneck in the mining paradigm. In essence, mining requests with explicitly-specified constraints cannot be handled in a way that is direct and precise. In this paper, we propose a direct mining framework to solve the problem and illustrate our ideas in the context of a particular type of constrained frequent patterns --- the "skinny" patterns, which are graph patterns with a long backbone from which short twigs branch out. These patterns, which we formally define as l-long δ-skinny patterns, are able to reveal insightful spatial and temporal trajectory patterns in mobile data mining, information diffusion, adoption propagation, and many others.