Accurate channel prediction is crucial for high-speed railway communications, especially in the 5G/6G era. Existing single-modality methods struggle to capture the intricate temporal and environmental dynamics, leading to suboptimal performance. To address this, we propose ChannelMTS, a novel multi-modal time-series framework that integrates both channel and environmental information to enhance prediction accuracy. First, ChannelMTS represents environmental conditions as snapshots, which are subsequently enhanced by a retrieval-augmented statistical channel module and embedded into an environmental time-series space using a transformer. Then, it aligns the channel and environmental time-series distributions to reduce the modality disparity. Finally, it adaptively fuses both modalities to achieve accurate channel prediction. This design can effectively leverage the complementary strengths of both modalities to enrich single-modality channel time series. Extensive experiments on real-world channel datasets show that ChannelMTS consistently outperforms state-of-the-art baselines. Moreover, online A/B testing reveals a significant 70%-90% performance improvement, and real-world deployment confirms its practical value.

Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools for modeling complex dynamics of multi-object physical systems. However, their generalization ability is limited by the inadequate consideration of physical inductive biases: (1) Existing studies overlook the continuity of transitions among system states, opting to employ several discrete transformation layers to learn the direct mapping between two adjacent states; (2) Most models only account for first-order velocity information, despite the fact that many physical systems are governed by second-order motion laws. To incorporate these inductive biases, we propose the Second-order Equivariant Graph Neural Ordinary Differential Equation (SEGNO). Specifically, we show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization. Additionally, we prove that the discrepancy between this learned trajectory of SEGNO and the true trajectory is bounded. Extensive experiments on complex dynamical systems including molecular dynamics and motion capture demonstrate that our model yields a significant improvement over the state-of-the-art baselines.

Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a single model is trained on vast and diverse datasets spanning multiple domains -- a paradigm we term 'All in One'. This methodology empowers LLMs with super generalization capabilities, facilitating an encompassing comprehension of varied data distributions. Leveraging these capabilities, a single LLM demonstrates remarkable versatility across a variety of domains -- a paradigm we term 'One for All'. However, applying this idea to the graph field remains a formidable challenge, with cross-domain pretraining often resulting in negative transfer. This issue is particularly important in few-shot learning scenarios, where the paucity of training data necessitates the incorporation of external knowledge sources. In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning. Our novel methodology involves a unification framework that amalgamates disparate graph datasets during the pretraining phase to distill and transfer meaningful knowledge to target tasks. Extensive experiments across multiple graph datasets demonstrate the superior efficacy of our approach. By successfully leveraging the synergistic potential of multiple graph datasets for pretraining, our work stands as a pioneering contribution to the realm of graph foundational model. Code available at https://github.com/cshhzhao/GCOPE.

Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional `Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings. Graph prompt learning emerges as a promising alternative, leveraging lightweight prompts to manipulate data and fill the task gap by reformulating downstream tasks to the pretext. However, several critical challenges still remain: how to unify diverse graph prompt models, how to evaluate the quality of graph prompts, and to improve their usability for practical comparisons and selection. In response to these challenges, we introduce the first comprehensive benchmark for graph prompt learning. Our benchmark integrates SIX pre-training methods and FIVE state-of-the-art graph prompt techniques, evaluated across FIFTEEN diverse datasets to assess performance, flexibility, and efficiency. We also present 'ProG', an easy-to-use open-source library that streamlines the execution of various graph prompt models, facilitating objective evaluations. Additionally, we propose a unified framework that categorizes existing graph prompt methods into two main approaches: prompts as graphs and prompts as tokens. This framework enhances the applicability and comparison of graph prompt techniques. The code is available at: https://github.com/sheldonresearch/ProG.

Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupervised learning, are hard to reach satisfactory detection accuracy due to the lack of labels. Weakly Supervised Anomaly Detection (WSAD) has been introduced with a limited number of labeled anomaly samples to enhance model performance. Nevertheless, it is still challenging for models, trained on an inadequate amount of labeled data, to generalize to unseen anomalies. In this paper, we introduce a novel framework, Knowledge-Data Alignment (KDAlign), to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data. Specifically, we transpose these rules into the knowledge space and subsequently recast the incorporation of knowledge as the alignment of knowledge and data. To facilitate this alignment, we employ the Optimal Transport (OT) technique. We then incorporate the OT distance as an additional loss term to the original objective function of WSAD methodologies. Comprehensive experimental results on five real-world datasets demonstrate that our proposed KDAlign framework markedly surpasses its state-of-the-art counterparts, achieving superior performance across various anomaly types. Our codes are released at https://github.com/cshhzhao/KDAlign.

Fault Scenario Identification (FSI) is a challenging task that aims to automatically identify the fault types in communication networks from massive alarms to guarantee effective fault recoveries. Existing methods are developed based on rules, which are not accurate enough due to the mismatching issue. In this paper, we propose an effective method named Knowledge-Enhanced Graph Neural Network (KE-GNN), the main idea of which is to integrate the advantages of both the rules and GNN. This work is the first work that employs GNN and rules to tackle the FSI task. Specifically, we encode knowledge using propositional logic and map them into a knowledge space. Then, we elaborately design a teacher-student scheme to minimize the distance between the knowledge embedding and the prediction of GNN, integrating knowledge and enhancing the GNN. To validate the performance of the proposed method, we collected and labeled three real-world 5G fault scenario datasets. Extensive evaluation conducted on these datasets indicates that our method achieves the best performance compared with other representative methods, improving the accuracy by up to 8.10%. Furthermore, the proposed method achieves the best performance against a small dataset setting and can be effectively applied to a new carrier site with a different topology structure.

This study deals with a joint channel selection and power allocation problem for time-varying underwater acoustic communication system. Without any prior channel information, designing a highly adaptable resource allocation algorithm to cope with the fast time-varying environment is a very challenging issue. To address this issue, a hierarchical learning approach, which is combined with adversarial multiarmed bandit theory and outdated pilot-based feedback information, is proposed. The proposed learning approach can online optimize joint resource allocate strategy without any prior channel state information. Specifically, a hierarchical self-reflection learning structure is proposed to offer different learning manners and spaces for the actual played information and outdated feedback information, thereby balancing the exploitation and exploration to cope with the time-varying environment effectively. Further, an integration learning structure is proposed to alleviate the solving difficulty and policy explosion of joint multiple substrategies problem. The user can rapidly achieve a few superior strategies in low-dimension space, then efficiently search the expected optimal strategy in high-dimension space, as a result, the learning efficiency is significantly improved. The proposed algorithms show strong tolerance for delay and noncomplete information due to the elaborate learning structures. The superiority of the proposed algorithms is demonstrated through numerical results.

In Internet of Underwater Things, data collection is assisted by autonomous underwater vehicle (AUV) to enhance the reliable transmission. AUV acts as a mobile collector and transmits the collected data to the station via relay nodes. However, the highly mobile nature of AUV needs an adaptive and efficient relay selection scheme for achieving good capacity performance. In this article, we propose a new contextual multiarmed bandit with evolving relay set (CMAB-ERS) learning framework, which successfully addresses crucial issues, including dynamic environment conditions and evolving relay set. To deal with the evolving relay set, CMAB-ERS incorporates collaborative effects into inference as well as learning processes, the new relays will acquire prior knowledge by having experienced nodes sharing observations, reducing the learning time significantly. To overcome the uncertainty of environmental information, we exploit the contextual environment factors to assist relay reward estimation and execute time-sensitive parameter update after every transmit–receive cycle, aiming for minimizing potential loss due to the time-varying channel. Correspondingly, the collaboration-aware online contextual bandit learning (COCBL) algorithm is designed that enables AUV to switch optimal relay adaptively and promises high-capacity transmission. Further, we rigorously prove the convergence of the COCBL algorithm by considering the evolving relay set and give its upper bound on the cumulative regret. Finally, extensive simulation results elucidate the effectiveness of the proposed COCBL.

Relay selection solutions for underwater acoustic cooperative networks suffer significant performance degradation as they fail to adapt to incomplete information, noisy interference and overwhelming dynamics. To address this challenge, a hierarchical adversarial multi-armed bandit learning framework by proposing an online reward estimation layer is designed to improve adaptive relay decision control. In online reward estimation layer, adaptive Kalman filter estimator is developed to properly handle noisy observation to support accurate reward. Meanwhile, an online predict mechanism is projected for all relays to enrich learning information. Furthermore, based on estimate error variance, an adaptive exploration structure is developed to accelerate the balance between exploration and exploitation. All gathered information are exploited to learn relay quality for the decision-making. Accordingly, we present a Hierarchical Adversarial Bandit Learning (HABL) algorithm to fully exploit the heuristic interaction between the hierarchical framework. HABL integrates reward estimation, information prediction, adaptive exploration and decision making carefully in a holistic algorithm to maximize the learning efficiency. Thereby, the HABL-based relay selection algorithm has higher system throughput and lower communication cost. Further, we rigorously analyze the convergence of HABL algorithm and give its upper bound on the cumulative regret. Finally, extensive simulations elucidate the effectiveness of the HABL.

Fault localization, as a crucial process in network fault management, is the process of deducing the exact source of a failure from a sequence of observed symptoms. Existing methods for this task are either expert system-based or data-driven. However, as communication networks grow and become more complex, conventional expert system-based approaches face problems of inefficiency and inflexibility. Besides, purely data-driven machine learning algorithms are not widely accepted in the industry because of their demand for large training sets and lack of explainability. Inspired by the dual process theory in psychology, we propose a dual-system method, named DualSys, for fault localization in this paper. In the proposed method, a fast data-driven intuitive system and a slow knowledge-driven logical system cooperate sequentially to fulfill the task. To avoid possible conflicts between the two systems, we further propose two conflict-easing mechanisms and incorporate them into the overall process. We validate our method using data from a real-world communication network. Experiment results indicate that our proposed method can get the same accuracy and explainability as knowledge-based approaches and achieve higher efficiency. As a result, we argue that our method provides network operators with a promising choice for efficient fault localization.

There are many business information systems under operation supporting the essential business processes of large scale multi-national enterprises, which forms the essential IT assets of these organizations. However, the isolation of these systems, improper process configurations and unbalanced resource allocations often leads to the deterioration of these enterprises, or even major management crisis. Service-oriented computing is considered a possible remedy for such issues with relatively low migration cost that has attracted much industrial attention. In this paper, through a survey to an industrial consulting case from a Southeastern Asia Garment Manufacture, we discuss how service requirements are elicited, how to evolve conventional management information systems into composite service systems that is reconfigurable at runtime, so as to realize resources optimization and efficiency improvement. The purpose of this paper is to analyze the know how of migrating conventional management information systems into service-oriented architecture, from a requirements engineering perspective.