Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Time series prediction is essential in many real-world applications, including power monitoring, traffic forecasting, and early warning of extreme weather events. While deep learning-based models have demonstrated strong performance in this domain, most existing approaches treat the input sequence holistically, failing to account for the distinct characteristics of its underlying components. This often hinders the extraction of deep, meaningful features. To address this issue, we propose a novel model, termed Seasonal-Trend decomposition based on Loess with Multi-Graph Attention Interaction (STL-MGAI), which integrates time series decomposition with component-specific graph modeling to improve both pattern learning and feature fusion. Specifically, STL decomposition is employed to extract three orthogonal components—trend, seasonal, and residual—which are then processed individually using adaptive graph convolution, a periodic-constrained graph attention network, and dynamic graph convolution, respectively. A feature fusion module, along with orthogonality constraints, ensures effective integration of these component-wise features while preserving their independence. Additionally, embedding this architecture within an Informer-based encoder-decoder framework enhances its capacity for long-range sequence modeling. Extensive experiments on both univariate and multivariate benchmarks demonstrate that STL-MGAI achieves superior accuracy and efficiency in long-term forecasting tasks. Notably, it improves prediction accuracy by 16.38% in energy consumption forecasting and by 19.52% in weather prediction. The model’s modular design and adaptive graph convolution also contribute to enhanced generalization and interpretability.

Sea surface temperature (SST) prediction is a critical task in ocean science, supporting various applications, such as weather forecasting, fisheries management, and storm tracking. While existing data-driven methods have demonstrated significant success, they often neglect to leverage the rich domain knowledge accumulated over the past decades, limiting further advancements in prediction accuracy. The recent emergence of large language models (LLMs) has highlighted the potential of integrating domain knowledge for downstream tasks. However, the application of LLMs to SST prediction remains under explored, primarily due to the challenge of integrating ocean domain knowledge and numerical data. To address this issue, we propose Ocean Knowledge Graph-enhanced LLM (OKG-LLM), a novel framework for global SST prediction. To the best of our knowledge, this work presents the first systematic effort to construct an Ocean Knowledge Graph (OKG) specifically designed to represent diverse ocean knowledge for SST prediction. We then develop a graph embedding network to learn the comprehensive semantic and structural knowledge within the OKG, capturing both the unique characteristics of individual sea regions and the complex correlations between them. Finally, we align and fuse the learned knowledge with fine-grained numerical SST data and leverage a pre-trained LLM to model SST patterns for accurate prediction. Extensive experiments on the real-world dataset demonstrate that OKG-LLM consistently outperforms state-of-the-art methods, showcasing its effectiveness, robustness, and potential to advance SST prediction. The codes are available in the online repository.

Advanced Persistent Threats (APTs) pose severe challenges to Next-Generation Communication Networks (NGCNs) due to their stealthiness and NGCNs’ dynamic topology, while conventional GNN-based intrusion detection systems suffer from static aggregation and poor adaptability to unseen nodes. To address these issues, this paper proposes DAA-FPN-SAGE, a lightweight graph-based detection framework integrating Dynamic Adaptive Aggregation (DAA) and Multi-Scale Feature Pyramid Network (MSFPM). Leveraging GraphSAGE’s inductive learning capability, the framework effectively models unseen nodes or subgraphs and adapts to NGCN’s dynamic changes (e.g., elastic network slicing, online AI model updates)—a key advantage for handling NGCN’s real-time topological variations. The DAA module employs multi-hop attention to dynamically assign weights to neighbors at different hop distances, enhancing capture of hierarchical dependencies in multi-stage APT attack chains. The MSFPM module fuses local-global structural information via a gated feature selection mechanism, resolving dimensional inconsistency and enriching attack behavior representation. Extensive experiments on StreamSpot, Unicorn, and DARPA TC#3 datasets demonstrate superior performance, meeting detection requirements of large-scale NGCNs.

Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurological impairments. However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcome these challenges, recent research has explored the integration of Large Language Models (LLMs), extending the focus from simple command decoding to understanding complex cognitive states.Despite these advancements, deploying agentic AI faces technical hurdles and ethical concerns.Due to the lack of comprehensive discussion on this emerging direction, this position paper argues that the field is poised for a paradigm extension from BCI to Brain-Agent Collaboration (BAC).We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors, demanding a focus on ethical data handling, model reliability, and a robust human-agent collaboration framework to ensure these systems are safe, trustworthy, and effective.

Abstract Link prediction, which aims to infer missing edges or predict future edges based on currently observed graph connections, has emerged as a powerful technique for diverse applications such as recommendation, relation completion, etc. While there is rich literature on link prediction based on node representation learning, direct link embedding is relatively less studied and less understood. One common practice in previous work characterizes a link by manipulate the embeddings of its incident node pairs, which is not capable of capturing effective link features. Moreover, common link prediction methods such as random walks and graph auto-encoder usually rely on full-graph training, suffering from poor scalability and high resource consumption on large-scale graphs. In this paper, we propose Inductive Subgraph Embedding for Link Prediciton (SE4LP) — an end-to-end scalable representation learning framework for link prediction, which utilizes the strong correlation between central links and their neighborhood subgraphs to characterize links. We sample the “link-centric induced subgraphs” as input, with a subgraph-level contrastive discrimination as pretext task, to learn the intrinsic and structural link features via subgraph classification. Extensive experiments on five datasets demonstrate that SE4LP has significant superiority in link prediction in terms of performance and scalability, when compared with state-of-the-art methods. Moreover, further analysis demonstrate that introducing self-supervision in link prediction can significantly reduce the dependence on training data and improve the generalization and scalability of model.

Thanks to the development of the Internet of Things, a large number of sensors have been deployed, resulting in the collection of abundant time series data. These data series contain potential space-time connection and noise at the same time. Prediction and health management (PHM) aim to provide decision support based on the health state of an entire engineering system. Additionally, the predicting future values also contribute to decision making and fall under the category of time series forecasting. While the existing methods focus on capturing the correlation between the data and reducing the impact of noise, they often fail to fully utilize the noise present in the time series data. In this article, we propose a framework for multivariate time series forecasting called GAGNN. This framework integrates the idea of a generative adversarial network and a graph neural network organically. It adopts the graph neural network as the generator and a multilayer perceptron as the discriminator. Finally, the prediction module is used to obtain the prediction results. The generator, discriminator, and prediction module are trained jointly. Our experimental results demonstrate that our model outperforms the original model on most benchmark data sets and achieves the best results on three out of six benchmark data sets.