GenAI4ELab/papercli-papers huggingface dataset:
Multi-Scale Temporal Neural Network for Stock Trend Prediction Enhanced by Temporal Hyepredge Learning
Authors: Lingyun Song; Haodong Li; Siyu Chen; Xinbiao Gan; Binze Shi; Jie Ma; Yudai Pan; Xiaoqi Wang; Xuequn Shang
Venue: IJCAI 2025 (main)

Abstract: Existing research in Stock Trend Prediction (STP) focuses on temporal features extracted from a temporal sequence of stock data with a look-back window, which frequently leads to the omission of important periodic patterns, such as weekly and monthly variations in stock prices. Furthermore, these methods examine stocks individually, ignoring the temporal variation patterns among stocks that share higher-order relationships, like those within the same industry. These relationships typically provide contextual insights into market investments influencing stock price fluctuations. To tackle these issues, we propose a Multi-Scale Temporal Neural Network (MSTNN) framework tailored for STP. This architecture explores the periodic fluctuation behaviors of individual stocks through an innovative 3D convolutional neural network, alongside examining temporal variation patterns of stocks linked to specific industries via a temporal hypergraph attention mechanism. Empirical results from two real-world benchmark datasets show that MSTNN significantly outperforms prior state-of-the-art STP methods. The code of our MSTNN is available at https://github.com/sunlitsong/MSTNN.
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
Authors: Yudan Song; Yuecen Wei; Yuhang Lu; Qingyun Sun; Minglai Shao; Li-e Wang; Chunming Hu; Xianxian Li; Xingcheng Fu
Venue: IJCAI 2025 (main)

Abstract: Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads to imbalanced transmission of global topological information and increased risk of node-specific information being overwhelmed during aggregation due to the imbalance between fraud and benign nodes. In this paper, we first summarize the impact of topology and class imbalance on downstream tasks in GNN-based fraud detection, as the problem of imbalanced supervisory messages is caused by fraudsters' topological behavior obfuscation and identity feature concealment. Based on statistical validation, we propose a novel dual-view graph representation learning method to mitigate Message imbalance in Fraud Detection (MimbFD). Specifically, we design a topological message reachability module for high-quality node representation learning to penetrate fraudsters' camouflage and alleviate insufficient propagation. Then, we introduce a local confounding debiasing module to adjust node representations, enhancing the stable association between node representations and labels to balance the influence of different classes. Finally, we conducted experiments on three public fraud datasets, and the results demonstrate that MimbFD exhibits outstanding performance in fraud detection.
DualCast: A Model to Disentangle Aperiodic Events from Traffic Series
Authors: Xinyu Su; Feng Liu; Yanchuan Chang; Egemen Tanin; Majid Sarvi; Jianzhong Qi
Venue: IJCAI 2025 (main)

Abstract: Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like traffic incidents. To address this, we propose DualCast, a dual-branch framework that disentangles traffic signals into intrinsic spatial-temporal patterns and external environmental contexts, including aperiodic events. DualCast also employs a cross-time attention mechanism to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets.
Towards Recognizing Spatial-temporal Collaboration of EEG Phase Brain Networks for Emotion Understanding
Authors: Jiangfeng Sun; Kaiwen Xue; Qika Lin; Yufei Qiao; Yifan Zhu; Zhonghong Ou; Meina Song
Venue: IJCAI 2025 (main)

Abstract: Emotion recognition from EEG signals is crucial for understanding complex brain dynamics. Existing methods typically rely on static frequency bands and graph convolutional networks (GCNs) to model brain connectivity. However, EEG signals are inherently non-stationary and exhibit substantial individual variability, making static-band approaches inadequate for capturing their dynamic properties. Moreover, spatial-temporal dependencies in EEG often lead to feature degradation during node aggregation, ultimately limiting recognition performance. To address these challenges, we propose the Spatial-Temporal Electroencephalograph Collaboration framework (Stella). Our approach introduces an Adaptive Bands Selection module (ABS) that dynamically extracts low- and high-frequency components, generating dual-path features comprising phase brain networks for connectivity modeling and time-series representations for local dynamics. To further mitigate feature degradation, the Fourier Graph Operator (FGO) operates in the spectral domain, while the Spatial-Temporal Encoder (STE) enhances representation stability and density. Extensive experiments on benchmark EEG datasets demonstrate that Stella achieves state-of-the-art performance in emotion recognition, offering valuable insights for graph-based modeling of non-stationary neural signals. The code is available at https://github.com/sun2017bupt/EEGBrainNetwork.
Trace: Structural Riemannian Bridge Matching for Transferable Source Localization in Information Propagation
Authors: Li Sun; Suyang Zhou; Bowen Fang; Hechuan Zhang; Junda Ye; Yutong Ye; Philip S. Yu
Venue: IJCAI 2025 (main)

Abstract: Source localization, the inverse problem of information diffusion, shows fundamental importance for understanding social dynamics. While achieving notable progress, existing solutions are typically exposed to the risk of error accumulation, and require a large number of observations for effective inference. However, it is often impractical to obtain quantities of observations in real scenarios, highlighting the need for a transferable model with broad applicability. Recently, Riemannian geometry has demonstrated its effectiveness in information diffusion and offers guidance in knowledge transfer, but has yet to be explored in source localization. In light of the issues above, we propose to study transferable source localization from a fresh geometric perspective, and present a novel approach (Trace) on the Riemannian manifold. Concretely, we establish a structural Schrodinger bridge to directly model the map between source and final distributions, where a functional curvature, encapsulating the graph structure, is formulated to govern the Schrodinger bridge and facilitate domain adaptation. Furthermore, we design a simple yet effective learning algorithm for Riemannian Schrodinger bridges (geodesics bridge matching) in which we prove the optimal projection holds for Riemannian measure so that the expensive iterative procedure is avoided. Extensive experiments demonstrate the effectiveness and transferability of Trace on both synthetic and real datasets.
Riding the Wave: Multi-Scale Spatial-Temporal Graph Learning for Highway Traffic Flow Prediction Under Overload Scenarios
Authors: Xigang Sun; Jiahui Jin; Hancheng Wang; Xiangguo Sun; Xiaoliang Wang; Jun Zhu
Venue: IJCAI 2025 (main)

Abstract: Highway traffic flow prediction under overload scenarios (HIPO) is a critical problem in intelligent transportation systems, which aims to forecast future traffic patterns on highway segments during periods of exceptionally high demand. Despite its importance, this problem has rarely been explored in recent research due to the unique challenges posed by irregular flow patterns, complex traffic behaviors, and sparse contextual data. In this paper, we propose a Heterogeneous Spatial-Temporal graph network With Adaptive contrastiVE learning (HST-WAVE) to address the HIPO problem. Specifically, we first construct a heterogeneous traffic graph according to the physical highway structure. Then, we develop a multi-scale temporal weaving Transformer and a coupled heterogeneous graph attention network to capture the irregular traffic flow patterns and complex transition behaviors. Furthermore, we introduce an adaptive temporal enhancement contrastive learning strategy to bridge the gap between divergent temporal patterns and mitigate data sparsity. We conduct extensive experiments on two real-world highway network datasets (No. G56 and G60 in Hangzhou, China), showing that our model can effectively handle the HIPO problem and achieve state-of-the-art performance. The source code is available at https://github.com/luck-seu/HST-WAVE.
SEP: A General Lossless Compression Framework with Semantics Enhancement and Multi-Stream Pipelines
Authors: Meng Wan; Rongqiang Cao; Yanghao Li; Jue Wang; Zijian Wang; Qi Su; Lei Qiu; Peng Shi; Yangang Wang; Chong Li
Venue: IJCAI 2025 (main)

Abstract: Deep-learning-based lossless compression is of immense importance in real-world applications, such as cold data persistence, sensor data collection, and astronomical data transmission. However, existing compressors typically model data using single-byte symbols as tokens, which makes it hard to capture the inherent correlations and cannot effectively utilize the parallel capabilities of GPU and multi-core CPU. This paper proposes SEP, a novel lossless compression framework for most time-series backbone neural networks. We first introduce a semantic enhancement module to capture the complex intra-patch relationships of binary byte streams. To improve the compression speed, we design multi-stream pipelines that dynamically assign parallel tasks to GPU streams and multi-cores. We further propose a novel GPU memory optimization strategy, which reuses GPU memory by a shared pool across streams. We conduct experiments on seven real-world datasets and the results demonstrate that our SEP framework outperforms state-of-the-art compressors with an average speed improvement of 30.0% and an average compression ratio gain of 5.1%, which is further elevated to 7.6% with the use of pre-training models. The GPU memory footprint is reduced by as high as 63.1% and by an average of 36.2%. The source code is available at: https://github.com/damonwan1/SEP.
Non-collective Calibrating Strategy for Time Series Forecasting
Authors: Bin Wang; Yongqi Han; Minbo  Ma; Tianrui Li; Junbo  Zhang; Feng  Hong; Yanwei Yu
Venue: IJCAI 2025 (main)

Abstract: Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make it challenging to establish the rule of thumb for designing the golden model architecture. In this study, we argue that refining existing advanced models through a universal calibrating strategy can deliver substantial benefits with minimal resource costs, as opposed to elaborating and training a new model from scratch. We first identify a multi-target learning conflict in the calibrating process, which arises when optimizing variables across time steps, leading to the underutilization of the model's learning capabilities. To address this issue, we propose an innovative calibrating strategy called Socket+Plug (SoP). This approach retains an exclusive optimizer and early-stopping monitor for each predicted target within each Plug while keeping the fully trained Socket backbone frozen. The model-agnostic nature of SoP allows it to directly calibrate the performance of any trained deep forecasting models, regardless of their specific architectures. Extensive experiments on various time series benchmarks and a spatio-temporal meteorological ERA5 dataset demonstrate the effectiveness of SoP, achieving up to a 22% improvement even when employing a simple MLP as the Plug (highlighted in Figure 1).
Distribution-Aware Online Learning for Urban Spatiotemporal Forecasting on Streaming Data
Authors: Chengxin Wang; Gary Tan; Swagato Barman Roy; Beng Chin  Ooi
Venue: IJCAI 2025 (main)

Abstract: The intrinsic non-stationarity of urban spatiotemporal (ST) streams, particularly unique distribution shifts that evolve over time, poses substantial challenges for accurate urban ST forecasting. Existing works often overlook these dynamic shifts, limiting their ability to adapt to evolving trends effectively. To address this challenge, we propose DOL, a novel Distribution-aware Online Learning framework designed to handle the unique shifts in urban ST streams. DOL introduces a streaming update mechanism that leverages streaming memories to strategically adapt to gradual distribution shifts. By aligning network updates with these shifts, DOL avoids unnecessary updates, reducing computational overhead while improving prediction accuracy. DOL also incorporates an adaptive spatiotemporal network with a location-specific learner, enabling it to handle diverse urban distribution shifts across locations. Experimental results on four real-world datasets confirm DOL's superiority over state-of-the-art models. The source code is available at https://github.com/cwang-nus/DOL.
ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Authors: Haichen Wang; Liu Yang; Xinyuan Zhang; Haomin Yu; Ming Li; Jilin Hu
Venue: IJCAI 2025 (main)

Abstract: Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively integrating these high-level correlations with the original correlations is crucial. To fill this gap, we propose the Aggregation Differential Transformer (ADFormer), which offers new insights to demand forecasting promotion. Specifically, we utilize Differential Attention to capture the original spatial correlations and achieve attention denoising. Meanwhile, we design distinct aggregation strategies based on the nature of space and time. Then, the original correlations are unified with the high-level correlations, enabling the model to capture holistic spatio-temporal relations. Experiments conducted on taxi and bike datasets confirm the effectiveness and efficiency of our model, demonstrating its practical value. The code is available at https://github.com/decisionintelligence/ADFormer.
RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming
Authors: Hao Wang; Jindong Han; Wei Fan; Leilei Sun; Hao Liu
Venue: IJCAI 2025 (main)

Abstract: Spatio-temporal forecasting is pivotal in numerous real-world applications, including transportation planning, energy management, and climate monitoring. In this work, we aim to harness the reasoning and generalization abilities of Pre-trained Language Models (PLMs) for more effective spatio-temporal forecasting, particularly in data-scarce scenarios. However, recent studies uncover that PLMs, which are primarily trained on textual data, often falter when tasked with modeling the intricate correlations in numerical time series, thereby limiting their effectiveness in comprehending spatio-temporal data. To bridge the gap, we propose RePST, a semantic-oriented PLM reprogramming framework tailored for spatio-temporal forecasting. Specifically, we first propose a semantic-oriented decomposer that adaptively disentangles spatially correlated time series into interpretable sub-components, which facilitates PLM to understand sophisticated spatio-temporal dynamics via a divide-and-conquer strategy. Moreover, we propose a selective discrete reprogramming scheme, which introduces an expanded spatio-temporal vocabulary space to project spatio-temporal series into discrete representations. This scheme minimizes the information loss during reprogramming and enriches the representations derived by PLMs. Extensive experiments on real-world datasets show that the proposed RePST outperforms twelve state-of-the-art baseline methods, particularly in data-scarce scenarios, highlighting the effectiveness and superior generalization capabilities of PLMs for spatio-temporal forecasting. Codes and Appendix can be found at https://github.com/usail-hkust/REPST.
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
Authors: Jinyan Wang; Liu Yang; Yuecen Wei; Jiaxuan Si; Chenhao Guo; Qingyun Sun; Xianxian Li; Xingcheng Fu
Venue: IJCAI 2025 (main)

Abstract: Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's prior knowledge of the sensitive attributes and realize membership inference attacks (MIA) by observing and analyzing the topology distribution. As privacy concerns grow, the assumption of MIA, which presumes that attackers can obtain an auxiliary dataset with the same distribution, is increasingly deviating from reality. In this paper, we categorize the distribution diversity issue in real-world MIA scenarios as an Out-Of-Distribution (OOD) problem, and propose a novel Graph OOD Membership Inference Attack (GOOD-MIA) to achieve cross-domain graph attacks. Specifically, we construct shadow subgraphs with distributions from different domains to model the diversity of real-world data. We then explore the stable node representations that remain unchanged under external influences and consider eliminating redundant information from confounding environments and extracting task-relevant key information to more clearly distinguish between the characteristics of training data and unseen data. This OOD-based design makes cross-domain graph attacks possible. Finally, we perform risk extrapolation to optimize the attack's domain adaptability during attack inference to generalize the attack to other domains. Experimental results demonstrate that GOOD-MIA achieves superior attack performance in datasets designed for multiple domains.
EVICheck: Evidence-Driven Independent Reasoning and Combined Verification Method for Fact-Checking
Authors: Lingxiao Wang; Lei Shi; Feifei Kou; Ligu Zhu; Chen Ma; Pengfei Zhang; Mingying Xu; Zeyu Li
Venue: IJCAI 2025 (main)

Abstract: Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have demonstrated significant potential in automated fact-checking. However, existing methods face limitations in insufficient evidence utilization and lack of explicit verification criteria. Specifically, these approaches aggregate evidence for collective reasoning without independently analyzing each piece, hindering their ability to leverage the available information thoroughly. Additionally, they rely on simple prompts or few-shot learning for verification, which makes truthfulness judgments less reliable, especially for complex claims. To address these limitations, we propose a novel method to enhance evidence utilization and introduce explicit verification criteria, named EVICheck. Our approach independently reasons each evidence piece and synthesizes the results to enable more thorough exploration and enhance interpretability. Additionally, by incorporating fine-grained truthfulness criteria, we make the model's verification process more structured and reliable, especially when handling complex claims. Experimental results on the public RAWFC dataset demonstrate that EVICheck achieves state-of-the-art performance across all evaluation metrics. Our method demonstrates strong potential in fake news verification, significantly improving the accuracy.
FreqLLM: Frequency-Aware Large Language Models for Time Series Forecasting
Authors: Shunnan Wang; Min Gao; Zongwei Wang; Yibing Bai; Feng Jiang; Guansong Pang
Venue: IJCAI 2025 (main)

Abstract: Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures multi-scale temporal patterns and provides more robust forecasting results. Extensive experiments on benchmark datasets demonstrate that FreqLLM outperforms state-of-the-art TSF methods in both accuracy and generalization. The code is available at https://github.com/biya0105/FreqLLM.
Higher-order Logical Knowledge Representation Learning
Authors: Suixue Wang; Weiliang Huo; Shilin Zhang; Qingchen Zhang
Venue: IJCAI 2025 (main)

Abstract: Real-world knowledge graphs abound with higher-order logical relations that simple triples, limited to pairwise connections, fail to represent. Thus, capturing higher-order logical relations involving multiple entities has garnered significant attention. However, existing methods ignore the structural information in higher-order relations. To this end, we propose a higher-order logical knowledge representation learning method, named LORE, which leverages network motifs, the patterns/subgraphs that naturally capture the structural information in graphs, to extract higher-order features and ultimately, learn effective representations of knowledge graphs. Compared to existing approaches, LORE aggregates the attribute features of entities with the extracted higher-order logical relations to form enhanced representations of knowledge graphs. In particular, three aggregators (i.e., Hadamard, Connection, and Summation) are proposed and employed. Extensive experiments have been conducted on six real-world datasets for two downstream tasks (i.e., entity classification and link prediction). The results show that LORE outperforms baselines significantly and consistently.