Spatial-Temporal Graph (STG) forecasting on large-scale networks has garnered significant attention. However, existing models predominantly focus on short-horizon predictions and suffer from notorious computational costs and memory consumption when scaling to long-horizon predictions and large graphs. Targeting the above challenges, we present FaST, an effective and efficient framework based on heterogeneity-aware Mixture-of-Experts (MoEs) for long-horizon and large-scale STG forecasting, which unlocks one-week-ahead (672 steps at a 15-minute granularity) prediction with thousands of nodes. FaST is underpinned by two key innovations. First, an adaptive graph agent attention mechanism is proposed to alleviate the computational burden inherent in conventional graph convolution and self-attention modules when applied to large-scale graphs. Second, we propose a new parallel MoE module that replaces traditional feed-forward networks with Gated Linear Units (GLUs), enabling an efficient and scalable parallel structure. Extensive experiments on real-world datasets demonstrate that FaST not only delivers superior long-horizon predictive accuracy but also achieves remarkable computational efficiency compared to state-of-the-art baselines. Our source code is available at: https://github.com/yijizhao/FaST.

Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series data across domains. While diffusion models have achieved remarkable success in Text-to-X (e.g., vision and audio data) generation, their use in time series generation remains limit. Existing approaches face two critical limitations: (1) reliance on domain-specific captions that generalize poorly, and (2) inability to generate time series of arbitrary length, limiting real-world use. In this work, we first introduce a new multimodal dataset containing over 600,000 high-resolution text-time series pairs. Second, we propose Text-to-Series (T2S), a diffusion-based framework that bridges the gap between natural language and time series in a domain-agnostic manner. It employs a length-adaptive VAE to encode time series of varying lengths into consistent latent embeddings. On top of that, T2S effectively aligns textual representations with latent embeddings by utilizing Flow Matching and employing DiT as the denoiser. We train T2S in an interleaved paradigm across multiple lengths, allowing it to generate sequences of arbitrary lengths. Extensive evaluations demonstrate that T2S achieves state-of-the-art performance across 13 datasets spanning 12 domains.

Various Web API Recommendation (AR) techniques have assisted developers in efficiently identifying suitable APIs for mashup creation. With the emergence of large language models (LLMs), there has been increasing interest in leveraging LLMs for recommender systems. Although several approaches have attempted to utilize LLMs by framing recommendations as prompts, this approach is not ideally suited for AR due to fundamental differences in the training processes of LLMs and AR models. Consequently, it's crucial to conduct further research to identify effective applications of LLMs in AR. To this end, we propose a novelLLM-based generative solution forAPIRecommendation (LLMAR) that combines instruction learning of multitask and multistage Low-Rank Adaptation fine-tuning based on LLaMA models. Experimental results on the ProgrammableWeb dataset show that LLMAR significantly outperforms representative methods in regular and data-limited scenarios.

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field.

Graph neural network (GNN) is a robust model for processing non-Euclidean data, such as graphs, by extracting structural information and learning high-level representations. GNN has achieved state-of-the-art recommendation performance on collaborative filtering (CF) for accuracy. Nevertheless, the diversity of the recommendations has not received good attention. Existing work using GNN for recommendation suffers from the accuracy-diversity dilemma, where slightly increases diversity while accuracy drops significantly. Furthermore, GNN-based recommendation models lack the flexibility to adapt to different scenarios' demands concerning the accuracy-diversity ratio of their recommendation lists. In this work, we endeavor to address the above problems from the perspective of aggregate diversity, which modifies the propagation rule and develops a new sampling strategy. We propose graph spreading network (GSN), a novel model that leverages only neighborhood aggregation for CF. Specifically, GSN learns user and item embeddings by propagating them over the graph structure, utilizing both diversity-oriented and accuracy-oriented aggregations. The final representations are obtained by taking the weighted sum of the embeddings learned at all layers. We also present a new sampling strategy that selects potentially accurate and diverse items as negative samples to assist model training. GSN effectively addresses the accuracy-diversity dilemma and achieves improved diversity while maintaining accuracy with the help of a selective sampler. Moreover, a hyper-parameter in GSN allows for adjustment of the accuracy-diversity ratio of recommendation lists to satisfy the diverse demands. Compared to the state-of-the-art model, GSN improved R @20 by 1.62%, N @20 by 0.67%, G @20 by 3.59%, and E @20 by 4.15% on average over three real-world datasets, verifying the effectiveness of our proposed model in diversifying overall collaborative recommendations.

Dynamic quality of service (QoS) measurement is crucial for discovering services and developing online service systems. Collaborative filtering-based approaches perform dynamic QoS prediction by incorporating temporal information only but never consider the dynamic network environment and suffer from poor performance. Considering different service invocation routes directly reflect the dynamic environment and further lead to QoS fluctuations, we coin the problem of Dynamic QoS Prediction (DQP) with Intelligent Route Estimation (IRE) and propose a novel framework named IRE4DQP. Under the IRE4DQP framework, the dynamic environment is captured by Network Status Representation, and the IRE is modeled as a Markov decision process and implemented by a deep learning agent. After that, the DQP is achieved by a specific neural model with the estimated route as input. Through collaborative training with reinforcement and inverse reinforcement learning, eventually, based on the updated representations of the network status, IRE learns an optimal route policy that matches well with observed QoS values, and DQP achieves accurate predictions. Experimental results demonstrate that IRE4DQP outperforms SOTA methods on the accuracy of response-time prediction by 5.79–31.34% in MAE, by 1.29–20.18% in RMSE, and by 4.43–27.73% in NMAE and with a success rate of nearly 45% on finding routes.

Accurate and reliable service quality prediction has become a key issue in service recommendation and network measurement scenarios. However, traditional methods for time-aware QoS prediction face two main challenges: (I) data sparsity makes it difficult to estimate and recover global information from the limited known data; (II) shallow learning models struggle to represent the intricate relationships between objects, and thus suffer poor prediction performance. To this end, we propose a time-aware QoS prediction framework that combines the merits of graph modeling, graph representation learning, and contrastive learning. First, a novel graph schema is proposed to capture the complex interactions between user-service-slots. Then, a prediction model is developed leveraging a graph convolutional network to learn the node representations by aggregating feature information from neighboring nodes. Finally, a novel contrastive learning strategy is used to improve the robustness of node representation. Experimental results on a large-scale dataset demonstrated that our proposed method significantly outperforms the state-of-the-art prediction methods on response time and throughput prediction tasks.

Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations; (ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations; and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novelSpatial-TemporalPosition-awareGraphConvolutionNetwork (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.

End-to-end QoS measurement plays an indispensable role in the decision-making of cloud services and IoT services. Many efforts have paid on developing QoS prediction approaches in the past decade leveraging the principle of collaborative filtering. But there remain many challenging issues concerning multi-task prediction requirements, feature selection for heterogeneous prediction tasks, and model training. To this end, we propose an effective personalized service QoS prediction method from the perspective of multi-task learning, named PMT. PMT consists of specially-designed feature selection components and a multi-step model training strategy. The feature selection method leverages the principle of multi-expert decision-making and self-attention mechanism. The multi-step model training enables a weight-free configuration for parallel prediction tasks. Experimental results on a large dataset with two tasks and a small dataset with three tasks demonstrate that PMT is superior to the state-of-the-art QoS prediction methods.

Pick-up and delivery (P&D) services such as food delivery have achieved explosive growth in recent years by providing customers with daily-life convenience. Though many service providers have invested considerably in routing tools, more and more practitioners realize that significant deviations exist between workers' actual routes and planned ones. So it is not wise to feed "optimal routes" as workers' actual service routes into downstream tasks (e.g., arrival-time prediction and order dispatching), whose performances count on the accuracy of route prediction, i.e., to predict the future service route of a worker's unfinished tasks. Therefore, to meet the rising calling for route prediction models that can capture workers' future routing behaviors, in this paper, we formulate the Pick-up and Delivery Route Prediction task (PDRP task for short) from the graph perspective for the first time, then propose a dynamic spatial-temporal graph-based model, named Graph2Route. Unlike previous sequence-based models, our model leverages the underlying graph structure and features into the encoding and decoding process. Moreover, the dynamic graph-based nature can spontaneously describe the evolving relationship between different problem instances. As a result, abundant decision context information and various spatial-temporal information of node/edge can be fully utilized in Graph2Route to improve the prediction performance. Offline experiments over two real-world industry-scale datasets under different P&D services (i.e., food delivery and package pick-up) and online A/B test demonstrate the superiority of our proposed model.

Forecasting traffic inflows and outflows is crucial for intelligent transportation applications such as traffic management and risk assessment. Recently, deep learning models, which focus on capturing spatio-temporal correlations between stations (locations) by constructing Spatio-Temporal Feature Learners (STFL), have achieved promising performance in traffic inflows and outflows prediction. However, two unresolved issues limit the performance of these models. i) dynamic and heterogeneous intra- and inter-relationships between flows are ignored, and ii) the STFL in these models cannot capture the global information. To address the above issues, we propose a novel deep Spatio-Temporal Network framework based on Multi-Relational learning (MR-STN) for predicting traffic inflows and outflows. Specifically, a multi-relational learning module is designed to comprehensively model three kinds of relationships between flows while extracting diverse spatio-temporal features. In this module, an enhanced STFL is developed to capture both local and global information. Then, a feature fusion module is introduced to extract fused features for inflows and outflows respectively via a gated fusion mechanism. On this basis, the prediction module uses fusion features to generate future inflows and outflows. Finally, we implement the proposed framework with four state-of-the-art graph-based deep spatio-temporal models to demonstrate its generality and superiority. Extensive experiments on three datasets show that the proposed framework can significantly boost the performance of existing models.

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

Label distribution learning (LDL) is a framework to solve label ambiguity. To improve the performance of LDL, some existing algorithms exploit global and local label correlations. In reality, local correlations are more reasonable than global ones because few correlations can be globally adapted to all samples. The existing algorithms exploiting local correlations assume that the smaller the Euclidean distance between samples, the more likely the samples are to share the same label correlations. Specifically, they all use K-means to divide samples into several clusters, and then mine local correlations in each cluster based on this assumption. However, this assumption is incorrect in some cases and may lead to inappropriate clustering results and biased correlations. In this paper, we propose a novel LDL algorithm which mines local label correlations in self-regulating clusters independent of sample distance. In particular, we introduce clustering with learnable parameters into the model to realize that the clustering can be optimized jointly with the objective function instead of depending on the distance between samples. In this way, our proposed algorithm can mine more accurate local label correlations in these more appropriate clusters. Experimental results on 15 real-world datasets demonstrate the effectiveness of the proposed algorithm.