Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments demonstrate that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in terms of imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.
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Balancing Imbalance: Data-Scarce Urban Flow Prediction via Spatio-Temporal Balanced Transfer Learning
PDF ↗Advanced deep spatio-temporal networks have become the mainstream for traffic prediction, but the widespread adoption of these models is impeded by the prevalent scarcity of available data. Despite cross-city transfer learning emerging as a common strategy to address this issue, it overlooks the inherent distribution imbalances within each city, which could potentially hinder the generalization capabilities of pre-trained models. To overcome this limitation, we propose a Spatio-Temporal Balanced Transfer Learning (STBaT) framework to enhance existing spatio-temporal prediction networks, ensuring both universality and precision in predictions for new urban environments. A Regional Imbalance Acquisition Module is designed to model the regional imbalances of source cities. Besides, to promote generalizable knowledge acquisition, a Spatio-Temporal Balanced Learning Module is devised to balance the predictive learning process. Extensive experiments on real-world datasets validate the efficacy of our proposed approach compared with several state-of-the-art methods.
Deep learning-based recommendation systems are increasingly important in the industry. To meet strict SLA requirements, serving frameworks must efficiently handle concurrent queries. However, current serving systems fail to serve concurrent queries due to the following problems: (1) inefficient operator (op) scheduling due to the query-wise op launching mechanism, and (2) heavy contention caused by the mutable nature of recommendation model inference. This paper presents RecOS, a system designed to optimize concurrent recommendation model inference on GPUs. RecOS efficiently schedules ops from different queries by monitoring GPU workloads and assigning ops to the most suitable streams. This approach reduces contention and enhances inference efficiency by leveraging inter-op parallelism and op characteristics. To maintain correctness across multiple CUDA streams, RecOS introduces a unified asynchronous tensor management mechanism. Evaluations demonstrate that RecOS improves online service performance, reducing latency by up to 68%.
Heterophily emerges as a critical challenge in Graph Anomaly Detection (GAD). Recent studies reveal that neighborhood distributions, rather than heterophily itself, are the fundamental factor for the expressive power of Graph Neural Networks (GNNs). However, two key challenges remain unresolved. First, the overlap in neighborhood distributions between anomalous and normal nodes poses significant difficulties in distinguishing them effectively. Second, the dispersion in neighborhood distributions within the same class prevents the application of a fixed aggregation strategy to accommodate the diverse patterns within the class. To tackle the aforementioned challenges, we propose a novel Graph Neural Network model called Neighborhood Adaptive Aggregation and Spectral Tuning (NAAST-GNN). Specifically, we first design a neighborhood adaptive aggregation module that adjusts the message passing mechanism based on the predicted probabilities for different node classes, ensuring that nodes from distinct classes but with similar neighborhood distributions derive unique aggregated neighborhood information. We then present a spectral tuning module that dynamically selects and combines spectral filters based on the predicted neighborhood distribution, ensuring adaptability to the diverse neighborhood distributions of nodes within the same class. Comprehensive experimental results demonstrate that our method outperforms state-of-the-art baselines.
Multi-modal learning (MML) is frequently hindered by modality imbalance, leading to suboptimal performance in real-world applications. To address this issue, existing approaches primarily focus on rebalancing MML from the perspective of optimization or architecture design. However, almost all existing methods ignore the impact of sample sequences, i.e., an inappropriate training order tends to trigger learning bias in the model, further exacerbating modality imbalance. In this paper, we propose Balance-aware Sequence Sampling (BSS) to enhance the robustness of MML. Specifically, we first define a multi-perspective measurer to evaluate the balance degree of each sample in terms of correlation and information criteria. Via this evaluation, we employ a heuristic scheduler based on curriculum learning (CL) that incrementally provides training subsets, progressing from balanced to imbalanced samples to alleviate the imbalance. Moreover, we propose a learning-based probabilistic sampling method to dynamically update the training sequence in a more fine-grained manner, further improving MML performance. Extensive experiments on widely used datasets demonstrate the superiority of our method compared with state-of-the-art (SOTA) baselines. The code is available at https://github.com/njustkmg/IJCAI25-BSS.
Graph Neural Networks (GNNs) have shown remarkable performance across various domains, yet they often struggle with model bias, particularly in the presence of class imbalance. This bias can lead to suboptimal performance and unfair predictions, especially for underrepresented classes. We introduce NeuBM (Neutral Bias Mitigation), a novel approach to mitigate model bias in GNNs through neutral input calibration. NeuBM leverages a dynamically updated neutral graph to estimate and correct the inherent biases of the model. By subtracting the logits obtained from the neutral graph from those of the input graph, NeuBM effectively recalibrates the model's predictions, reducing bias across different classes. Our method integrates seamlessly into existing GNN architectures and training procedures, requiring minimal computational overhead. Extensive experiments on multiple benchmark datasets demonstrate that NeuBM significantly improves the balanced accuracy and recall of minority classes, while maintaining strong overall performance. The effectiveness of NeuBM is particularly pronounced in scenarios with severe class imbalance and limited labeled data, where traditional methods often struggle. We provide theoretical insights into how NeuBM achieves bias mitigation, relating it to the concept of representation balancing. Our analysis reveals that NeuBM not only adjusts the final predictions but also influences the learning of balanced feature representations throughout the network.
Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming
PDF ↗Feature transformation involves generating a new set of features from the original dataset to enhance the data's utility. In certain domains like material performance screening, dimensionality is large and collecting labels is expensive and lengthy. It highly necessitates transforming feature spaces efficiently and without supervision to enhance data readiness and AI utility. However, existing methods fall short in efficient navigation of a vast space of feature combinations, and are mostly designed for supervised settings. To fill this gap, our unique perspective is to leverage a generator-critic duet-play teaming framework using LLM agents and in-context learning to derive pseudo-supervision from unsupervised data. The framework consists of three interconnected steps: (1) Critic agent diagnoses data to generate actionable advice, (2) Generator agent produces tokenized feature transformations guided by the critic's advice, and (3) Iterative refinement ensures continuous improvement through feedback between agents. The generator-critic framework can be generalized to human-agent collaborative generation, by replacing the critic agent with human experts. Extensive experiments demonstrate that the proposed framework outperforms even supervised baselines in feature transformation efficiency, robustness, and practical applicability across diverse datasets. Our code is publicly available at https://github.com/NanxuGong/LPFG.
Traditional knowledge graphs (KGs) provide each entity with a unique embedding as a representation, which contains a lot of redundant information. Meanwhile, the space complexities of the KGs are positively related to the number of entities. In this work, we propose a hierarchical representation learning method, namely HRL, which is a parameter-efficient model where the number of model parameters is independent of dataset scales. Specifically, we propose a hierarchical model comprising a Meta Encoder and a Context Encoder to generate the representation of entities and relations. The Meta Encoder captures the common representations shared across entities, while the Context Encoder learns entity-specific representations. We further provide a theoretical analysis of model design by constructing a structural causal model (SCM) when completing a knowledge graph. The SCM outlines the relationships between nodes, where entity embeddings are conditioned on both common and entity-specific representations. Note that our model is designed to reduce model scale while maintaining competitive performance. We evaluate HRL on the knowledge graph completion task using three real-world datasets. The results demonstrate that HRL significantly outperforms existing parameter-efficient baselines, as well as traditional state-of-the-art baselines of similar scale.
Memory-based temporal graph neural networks (MTGNN) use node memory to store historical information, enabling efficient processing of large dynamic graphs through batch parallel training, with larger batch sizes leading to increased training efficiency. However, this approach overlooks the interdependency among edges within the same batch, leading to outdated memory states and reduced training accuracy. Previous studies have attempted to mitigate this issue through methods such as measuring memory loss, overlap training, and additional compensation modules. Despite these efforts, challenges persist, including imprecise coarse-grained memory loss measurement and ineffective compensation modules. To address these challenges, we propose the Refined Batch parallel Training (RBT) framework, which accurately evaluates intra-batch information loss and optimizes batch partitioning to minimize loss, enhancing the training process's effectiveness and efficiency. RBT also includes a precise and efficient memory compensation algorithm. Experimental results demonstrate RBT's superior performance compared to existing MTGNN frameworks like TGL, ETC, and PRES in terms of training efficiency and accuracy across various dynamic graph datasets. Our code is made publicly available at https://github.com/fengwudi/RBT.
Exploiting Self-Refining Normal Graph Structures for Robust Defense against Unsupervised Adversarial Attacks
PDF ↗Defending against adversarial attacks on graphs has become increasingly important. Graph refinement to enhance the quality and robustness of representation learning is a critical area that requires thorough investigation. We observe that representations learned from attacked graphs are often ineffective for refinement due to perturbations that cause the endpoints of perturbed edges to become more similar, complicating the defender's ability to distinguish them. To address this challenge, we propose a robust unsupervised graph learning framework that utilizes cleaner graphs to learn effective representations. Specifically, we introduce an anomaly detection model based on contrastive learning to obtain a rough graph excluding a large number of perturbed structures. Subsequently, we then propose the Graph Pollution Degree (GPD), a mutual information-based measure that leverages the encoder's representation capability on the rough graph to assess the trustworthiness of the predicted graph and refine the learned representations. Extensive experiments on four benchmark datasets demonstrate that our method outperforms nine state-of-the-art defense models, effectively defending against adversarial attacks and enhancing node classification performance.
Picturized and Recited with Dialects: A Multimodal Chinese Representation Framework for Sentiment Analysis of Classical Chinese Poetry
PDF ↗Classical Chinese poetry is a vital and enduring part of Chinese literature, conveying profound emotional resonance. Existing studies analyze sentiment based on textual meanings, overlooking the unique rhythmic and visual features inherent in poetry,especially since it is often recited and accompanied by Chinese paintings. In this work, we propose a dialect-enhanced multimodal framework for classical Chinese poetry sentiment analysis. We extract sentence-level audio features from the poetry and incorporate audio from multiple dialects,which may retain regional ancient Chinese phonetic features, enriching the phonetic representation. Additionally, we generate sentence-level visual features, and the multimodal features are fused with textual features enhanced by LLM translation through multimodal contrastive representation learning. Our framework outperforms state-of-the-art methods on two public datasets, achieving at least 2.51% improvement in accuracy and 1.63% in macro F1. We open-source the code to facilitate research in this area and provide insights for general multimodal Chinese representation.
Suit the Node Pair to the Case: A Multi-Scale Node Pair Grouping Strategy for Graph-MLP Distillation
PDF ↗Graph Neural Network (GNN) is powerful in solving various graph-related tasks, while its message passing mechanism may lead to latency during inference time. Multi-Layer-Perceptron (MLP) can achieve fast inference speed but with limited performance. One solution to fill this gap is through Knowledge Distillation. However, current distillation methods follow a ''node-to-node'' paradigm, while considering the complex relationships between different node pairs, direct distillation fails to capture these multiple-granularity features in GNN. Furthermore, current methods which focuses on the alignment of logits in the final layer ignores further learning within layers inside student MLP. Therefore, in this paper, we introduce a multi-scale knowledge distillation method (MSN-GDM) aiming to capture multiple knowledge from GNN to MLP. We firstly propose a multi-scale node-pair grouping strategy to assign node pairs to different-scale groups according to node pair similarity metrics. The similarity metrics consider both node features and topological structures of the given node pair. Then based on the preprocessed node-set groups, we design a multi-scale distillation method that can capture comprehensive knowledge in the corresponding node-set groups. The hierarchical weighted sum of each layer is applied as the final output. Extensive experiments on eight real-world datasets demonstrate the effectiveness of our proposed method.
Graph unlearning, which deletes graph elements such as nodes and edges from trained graph neural networks (GNNs), is crucial for real-world applications where graph data may contain outdated, inaccurate, or privacy-sensitive information. However, existing methods often suffer from (1) incomplete or over unlearning due to neglecting the distinct objectives of different unlearning tasks, and (2) inaccurate identification of neighbors affected by deleted elements across various GNN architectures. To address these limitations, we propose AGU, a novel Adaptive Graph Unlearning framework that flexibly adapts to diverse unlearning tasks and GNN architectures. AGU ensures the complete forgetting of deleted elements while preserving the integrity of the remaining graph. It also accurately identifies affected neighbors for each GNN architecture and prioritizes important ones to enhance unlearning performance. Extensive experiments on seven real-world graphs demonstrate that AGU outperforms existing methods in terms of effectiveness, efficiency, and unlearning capability.
HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion
PDF ↗Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.
SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs
PDF ↗Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.
A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams
PDF ↗Online Anomaly Detection (OAD) is critical for identifying rare yet important data points in large, dynamic, and complex data streams. A key challenge lies in achieving accurate and consistent detection of anomalies while maintaining computational and memory efficiency. Conventional OAD approaches, which depend on distributional deviations and static thresholds, struggle with model update delays and catastrophic forgetting, leading to missed detections and high false positive rates. To address these limitations, we propose a novel Streaming Anomaly Detection (SAD) method, grounded in a sparse active online learning framework. Our approach uniquely integrates ℓ1,2-norm sparse online learning with CUR decomposition-based active learning, enabling simultaneous fast feature selection and dynamic instance selection. The efficient CUR decomposition further supports real-time residual analysis for anomaly scoring, eliminating the need for manual threshold settings about temporal data distributions. Extensive experiments on diverse streaming datasets demonstrate SAD's superiority, achieving a 14.06% reduction in detection error rates compared to five state-of-the-art competitors.
FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis
PDF ↗In this paper, we address the task of targeted sentiment analysis , which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresponding senti-ments. Aspect extraction forms the foundation for sentiment prediction, highlighting the critical dependency between these two tasks for effective cross-task knowledge transfer. While most existing studies adopt a multi-task learning paradigm to align task-specific features in the latent space, they predominantly rely on coarse-grained knowledge transfer. Such approaches lack fine-grained control over aspect-sentiment relationships, often assuming uniform sentiment polarity within related aspects. This oversimplification neglects contextual cues that differentiate sentiments, leading to negative transfer. To overcome these limitations, we propose FCKT, a fine-grained cross-task knowledge transfer framework tailored for TSA. By explicitly incorporating aspect-level information into sentiment prediction, our framework achieves fine-grained knowledge transfer, effectively mitigating negative transfer and enhancing task performance. Extensive experiments on three real-world datasets, including comparisons with various baselines and large language models (LLMs), demonstrate the effectiveness of FCKT. The source code is available on https://github.com/cwei01/FCKT.
Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
PDF ↗Antibody design remains a critical challenge in therapeutic and diagnostic development, particularly for complex antigens with diverse binding interfaces. Current computational methods face two main limitations: (1) capturing geometric features while preserving symmetries, and (2) generalizing novel antigen interfaces. Despite recent advancements, these methods often fail to accurately capture molecular interactions and maintain structural integrity. To address these challenges, we propose AbMEGD, an end-to-end framework integrating Multi-scale Equivariant Graph Diffusion for antibody sequence and structure co-design. Leveraging advanced geometric deep learning, AbMEGD combines atomic-level geometric features with residue-level embeddings, capturing local atomic details and global sequence-structure interactions. Its E(3)-equivariant diffusion method ensures geometric precision, computational efficiency, and robust generalizability for complex antigens. Furthermore, experiments using the SAbDab database demonstrate a 10.13% increase in amino acid recovery, 3.32% rise in improvement percentage, and a 0.062 Å reduction in root mean square deviation within the critical CDR-H3 region compared to DiffAb, a leading antibody design model. These results highlight AbMEGD's ability to balance structural integrity with improved functionality, establishing a new benchmark for sequence-structure co-design and affinity optimization. The code is available at: https://github.com/Patrick221215/AbMEGD.
Streaming Recommendation (SRec) aims to capture evolving user preferences in the streaming scenarios. Recently, Graph Prompt Learning (GPL) methods have demonstrated their effectiveness and adaptability within SRec. However, existing graph prompt solutions rarely consider the evolution of multi-hop cascading relationships between users and items, which are crucial for modeling the shifts in user preferences. To address this problem, we propose a novel Graph Multi-Level Aware Prompt Learning for Streaming Recommendation, named GPL4SRec. Specifically, a graph encoder is first pre-trained on extensive historical data to capture user long-term preferences. Then, we design three types of prompts, namely node-aware, structure-aware, and layer-aware prompts, which are used to guide the pre-trained encoder to better capture user short-term preferences. This is accomplished by accounting for both the incremental changes in users and items, as well as the cascading evolution in multi-hop relationships. Furthermore, we provide a theoretical analysis showing that our prompt templates are critical to achieving superior performance. Finally, experimental results also prove that our model significantly outperforms the state-of-the-art approaches in SRec.
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning mechanism for the classifier, the proposed AKBR approach aims to define an end-to-end representation learning model to construct an adaptive kernel matrix for graphs. To this end, we commence by leveraging a novel feature-channel attention mechanism to capture the interdependencies between different substructure invariants of original graphs. The proposed AKBR model can thus effectively identify the structural importance of different substructures, and compute the R-convolution kernel between pairwise graphs associated with the more significant substructures specified by their structural attentions. Furthermore, the proposed AKBR model employs all sample graphs as the prototype graphs, naturally providing an end-to-end learning architecture between the kernel computation as well as the classifier. Experimental results show that the proposed AKBR model outperforms existing state-of-the-art graph kernels and deep learning methods on standard graph benchmarks.