OpenRaiser/Intern-Atlas huggingface dataset:
TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors
Authors: Jingyi Zheng, Junfeng Wang, Zhen Sun, Wenhan Dong, Yule Liu, Xinlei He
Venue: KDD
Year: 2025

As Large Language Models (LLMs) advance, Machine-Generated Texts (MGTs) have become increasingly fluent, high-quality, and informative. Existing wide-range MGT detectors are designed to identify MGTs to prevent the spread of plagiarism and misinformation. However, adversaries attempt to humanize MGTs to evade detection (named evading attacks), which requires only minor modifications to bypass MGT detectors. Unfortunately, existing attacks generally lack a unified and comprehensive evaluation framework, as they are assessed using different experimental settings, model architectures, and datasets. To fill this gap, we introduce the Text-Humanization Benchmark (TH-Bench), the first comprehensive benchmark to evaluate evading attacks against MGT detectors. TH-Bench evaluate attacks across three key dimensions: evading effectiveness, text quality, and computational overhead. Our extensive experiments evaluate 6 state-of-the-art attacks against 13 MGT detectors across 6 datasets, spanning 19 domains and generated by 11 widely used LLMs. Our findings reveal that no single evading attack excels across all three dimensions. Through in-depth analysis, we highlight the strengths and limitations of different attacks. More importantly, we identify a trade-off among three dimensions and propose two optimization insights. Through preliminary experiments, we validate their correctness and effectiveness, offering potential directions for future research.
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
Authors: Bowen Zheng, Junjie Zhang, Hongyu Lu, Yu Chen, Ming Chen, Wayne Xin Zhao, Ji-Rong Wen
Venue: KDD
Year: 2025

Graph neural network (GNN) has been a powerful approach in collaborative filtering (CF) due to its ability to model high-order user-item relationships. Recently, to alleviate the data sparsity and enhance representation learning, many efforts have been conducted to integrate contrastive learning (CL) with GNNs. Despite the promising improvements, the contrastive view generation based on structure and representation perturbations in existing methods potentially disrupts the collaborative information in contrastive views, resulting in limited effectiveness of positive alignment. To overcome this issue, we propose CoGCL, a novel framework that aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes. The core idea is to map users and items into discrete codes rich in collaborative information for reliable and informative contrastive view generation. To this end, we initially introduce a multi-level vector quantizer in an end-to-end manner to quantize user and item representations into discrete codes. Based on these discrete codes, we enhance the collaborative information of contrastive views by considering neighborhood structure and semantic relevance respectively. For neighborhood structure, we propose virtual neighbor augmentation by treating discrete codes as virtual neighbors, which expands an observed user-item interaction into multiple edges involving discrete codes. Regarding semantic relevance, we identify similar users/items based on shared discrete codes and interaction targets to generate the semantically relevant view. Through these strategies, we construct contrastive views with stronger collaborative information and develop a triple-view graph contrastive learning approach. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed approach. Moreover, detailed analyses highlight our contribution in enhancing graph CL for recommendation. Our code is available at https://github.com/RUCAIBox/CoGCL.
Multi-scale Physics-informed Transformer With Spatio-temporal Feature Adapter For Extreme Precipitation Nowcasting
Authors: Jingyuan Zheng, Xin Zhang, Zhilin Qi, Ruiang Qiu, Dongjing Wang, Haiping Zhang, Dongjin Yu
Venue: KDD
Year: 2025

Extreme precipitation, as a core causative factor of meteorological disasters, poses significant challenges for accurate short-term forecasting due to the chaotic nature of precipitation systems and their multi-scale spatio-temporal evolution. Traditional numerical models are notably affected by error accumulation, while existing deep learning models still face dual limitations in physical fidelity and multi-scale feature extraction. To Address these issues, we propose an innovative Multi-scale Physics-informed Transformer with spatio-temporal feature adapter for extreme precipitation nowcasting, termed MPFormer. Our framework comprises two core components: the deterministic Evolution Network and the stochastic Generative Network. The Evolution Network integrates a novel Scale-Aware Temporal Residual Modulation Transformer (STRMT) encoder that captures multi-scale storm dynamics through residual temporal attention. The Generative Network introduces spatio-temporal adapters as lightweight transfer modules for probabilistic modeling. We develop a Multi-scale Physics-informed Loss with three innovations: 1) dynamic weight scheduling for feature fusion, 2) physical constraints preserving storm evolution patterns, and 3) entropy-based uncertainty calibration. Experiments based on MRMS radar data from North America demonstrate that the model can generate high-resolution forecasts (2km grid) with a 3-hour lead time over an area of 2048×2048 square kilometers. Compared to the state-of-the-art technologies, the proposed framework shows significant effectiveness and superiority in metrics such as CSIN, offering a new paradigm that combines physical interpretability with engineering practicality for extreme weather warnings and disaster prevention in smart cities.
A Fast and Accurate Block Compression Solution for Spatiotemporal Kernel Density Visualization
Authors: Yue Zhong, Tsz Nam Chan, Leong Hou U, Dingming Wu, Wei Tu, Ruisheng Wang, Joshua Zhexue Huang
Venue: KDD
Year: 2025

Spatiotemporal Kernel Density Visualization (STKDV) has been widely used across various domains in geospatial analysis, e.g., urban planning, traffic/traffic accident hotspot analysis, crime hotspot analysis, and disease spread modeling. However, STKDV is a computationally expensive tool, which has been complained by many domain experts. Although many recent solutions, including the sliding-window-based solution (SWS) and the prefix-matrix-based solution (PREFIX), have been proposed for improving the efficiency of generating an exact STKDV, these solutions still cannot be scalable to handle large-scale location datasets. To tackle this efficiency issue, we propose the pioneering block compression solution, called COMP, which can compress (or represent) a location dataset by a small amount of blocks. By combining COMP with the existing exact solutions, i.e., SWS and PREFIX, we show that COMPSWS and COMPPREFIX can generate approximate STKDV with an ε-absolute error guarantee based on properly tuning the block size. Experimental results on four large-scale location datasets (up to 6.782 million data points) also verify that COMPSWS and COMP_PREFIX can achieve speedups of 4.1x to 677.16x and 1.45x to 143.52x compared with SWS and PREFIX, respectively, without degrading the visualization results. The code of this paper can be found in https://github.com/YovelaZ/COMP.
GuardFGL: Similarity-driven Federated Graph Learning with Adversarial Robustness and Membership Privacy
Authors: Luying Zhong, Xuan Lai, Junjie Zhang, Zhiqin Huang, Zheyi Chen
Venue: KDD
Year: 2025

The emerging Federated Graph Learning (FGL) offers promising collaborative training on distributed graph data. However, malicious actors may contaminate data streams by falsifying node relationships on clients or conduct adversarial attacks on edge servers, causing degraded inference and privacy leakage. Although some studies focus on privacy-protection FGL, they do not consider robustness and membership privacy amidst data pollution and adversarial attacks. Moreover, classic FGL commonly adopts FedAvg but neglects the impact of uneven information flow from distinct subtopologies. To address these important challenges, we propose GuardFGL, a novel similarity-driven FGL that extracts minimal-sufficient information from polluted data to maintain strong adversarial robustness and protect membership privacy. First, we incorporate structural-aware and feature-selection learning to explore target-relevant edges and features, avoiding privacy leakage from raw data. Next, we design an original Federated Graph Information Bottleneck (FGIB) principle to supervise extracting well-compressed information, mitigating the interference of polluted data streams. Finally, we develop a similarity-driven federated aggregation with auxiliary local information to alleviate the impact of uneven information flow. Using the real-world testbed and benchmark graph datasets, extensive experiments demonstrate that GuardFGL can achieve superior robust prediction and better protect membership privacy than state-of-the-art methods under adversarial attacks.
MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification
Authors: Shuhan Zhong, Weipeng Zhuo, Sizhe Song, Guanyao Li, Zhongyi Yu, S.-H. Gary Chan
Venue: KDD
Year: 2025

Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can lead to poor channel-wise modeling of existing deep learning methods. To overcome this limitation, we propose MTM, a multi-scale token mixing transformer for the classification of IMTS. We find that the channel-wise asynchrony can be alleviated by down-sampling the time series to coarser timescales, and propose to incorporate a masked concat pooling in MTM that gradually down-samples IMTS to enhance the channel-wise attention modules. Meanwhile, we propose a novel channel-wise token mixing mechanism which proactively chooses important tokens from one channel and mixes them with other channels, to further boost the channel-wise learning of our model. Through extensive experiments on real-world datasets and comparison with state-of-the-art methods, we demonstrate that MTM consistently achieves the best performance on all the benchmarks, with improvements of up to 3.8% in AUPRC for classification.
Position-Aware Neighbor Aggregation for Dynamic Link Prediction
Authors: Yumeng Zhou, Mingzhe Liu, Leilei Sun, Yifei Huang, Liangzhe Han, Chuanren Liu, Tongyu Zhu
Venue: KDD
Year: 2025

Dynamic link prediction, which aims to predict future interactions based on historical temporal graph evolution, plays a pivotal role in applications such as social networks, recommendation systems, and patent trend prediction. However, existing research predominantly validates model performance under a single data distribution characterized by high-density repeated interactions, which leads to an insufficient exploration of higher-order node relationships. In contrast, we argue that dynamic graphs exhibit diverse application potential, where the occurrence of future interactions is not exclusively determined by historical repeated events, but rather by higher-order positional dependencies. To fill this gap, we classify the data into two scenarios with dense and sparse repeated interactions, and encode the temporal positions of neighbors relative to the event center to better guide hierarchical neighborhood aggregation. Building upon this, we propose PANet, a position-aware dynamic graph network that captures the temporal positioning of nodes in evolving interactions. Firstly, we define the spatial and temporal position of neighbors based on their distance to the event center and their occurrence frequency. Secondly, we employ a hierarchical paired-position encoding approach to compute the temporal position representation for each neighbor. Last, we leverage the relative positions and timestamps of neighbors to guide the aggregation of neighborhood information, and derive the final node representation through an attention-based network. Experiments conducted on eight datasets across two distributions validate the effectiveness and efficiency of the proposed method. Moreover, the significant performance improvement achieved by incorporating the position-aware neighbor aggregation into existing dynamic graph methods further demonstrates its versatility.
Blurred Encoding for Trajectory Representation Learning
Authors: Silin Zhou, Yao Chen, Shuo Shang, Lisi Chen, Bingsheng He, Ryosuke Shibasaki
Venue: KDD
Year: 2025

Trajectory representation learning (TRL) maps trajectories to vector embeddings and facilitates tasks such as trajectory classification and similarity search. State-of-the-art (SOTA) TRL methods transform raw GPS trajectories to grid or road trajectories to capture high-level travel semantics, i.e., regions and roads. However, they lose fine-grained spatial-temporal details as multiple GPS points are grouped into a single grid cell or road segment. To tackle this problem, we propose the BLU rred Encoding method, dubbed BLUE, which gradually reduces the precision of GPS coordinates to create hierarchical patches with multiple levels. The low-level patches are small and preserve fine-grained spatial-temporal details, while the high-level patches are large and capture overall travel patterns. To complement different patch levels with each other, our BLUE is an encoder-decoder model with a pyramid structure. At each patch level, a Transformer is used to learn the trajectory embedding at the current level, while pooling prepares inputs for the higher level in the encoder, and up-resolution provides guidance for the lower level in the decoder. BLUE is trained using the trajectory reconstruction task with the MSE loss. We compare BLUE with 8 SOTA TRL methods for 3 downstream tasks, the results show that BLUE consistently achieves higher accuracy than all baselines, outperforming the best-performing baselines by an average of 30.90%. Our code is available at https://github.com/slzhou-xy/BLUE.
Contrastive Text-enhanced Transformer for Cross-Domain Sequential Recommendation
Authors: Donglin Zhou, Xinbei Cai, Weike Pan
Venue: KDD
Year: 2025

Cross-domain sequential recommendation (CDSR) aims to enhance recommendation performance in a target domain by leveraging user sequential preferences from a source domain. As most advanced methods employ various neural networks to model item-ID sequences, they often struggle with a sparse data. Recent text-based CDSR approaches utilize rich text information to facilitate knowledge transfer across domains but still encounter three main challenges: (i) how to capture domain-shared semantic relationships; (ii) how to effectively integrate semantic intents and behavior intents; and (iii) how to design sufficient supervision signals for model training. In this paper, we propose a novel text-enhanced CDSR solution, i.e., contrastive text-enhanced Transformer (CTT), which jointly learns user preferences from semantic and behavioral information. Specifically, our CTT is a dual-view structure, including an inter-domain preference view and an intra-domain preference view. In the former, we design a domain-shared cross-attention mechanism to encode semantic correlations across different domains by an attention map, which is then utilized for cross-domain semantic intent learning. In the latter, we extract fine-grained behavior intents from item-ID sequences and coarse-grained semantic intents from item-text sequences, thereby capturing high-quality intra-domain preferences. Our CTT integrates these two types of preferences to predict the next item for each user. Moreover, we propose a semantic-behavior contrastive learning approach to enhance cross-domain preference learning and knowledge transfer. Extensive experiments on three real-world datasets demonstrate that our CTT outperforms the state-of-the-art models by an average of 5.86% on HR@5. The scripts for data preprocessing and conducting experiments, the parameter configurations, and the source codes of our CTT and all baselines are available at https://github.com/XinbeiCai/CTT.
Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating
Authors: Yiyun Zhou, Zheqi Lv, Shengyu Zhang, Jingyuan Chen
Venue: KDD
Year: 2025

Knowledge Tracing (KT) is a core component of Intelligent Tutoring Systems, modeling learners' knowledge state to predict future performance and provide personalized learning support. Traditional KT models assume that learners' learning abilities remain relatively stable over short periods or change in predictable ways based on prior performance. However, in reality, learners' abilities change irregularly due to factors like cognitive fatigue, motivation, and external stress--a task introduced, which we refer to as Real-time Learning Pattern Adjustment (RLPA). Existing KT models, when faced with RLPA, lack sufficient adaptability, because they fail to timely account for the dynamic nature of different learners' evolving learning patterns. Current strategies for enhancing adaptability rely on retraining, which leads to significant overfitting and high time overhead issues. To address this, we propose Cuff-KT, comprising a controller and a generator. The controller assigns value scores to learners, while the generator generates personalized parameters for selected learners. Cuff-KT controllably adapts to data changes fast and flexibly without fine-tuning. Experiments on five datasets from different subjects demonstrate that Cuff-KT significantly improves the performance of five KT models with different structures under intra- and inter-learner shifts, with an average relative increase in AUC of 10% and 4%, respectively, at a negligible time cost, effectively tackling RLPA task. Our code and datasets are fully available at https://github.com/zyy-2001/Cuff-KT.
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning
Authors: Yu Zhou, Bingyan Liu
Venue: KDD
Year: 2025

Federated Learning (FL) enables multiple clients to collaboratively develop a global model while maintaining data privacy. However, online FL deployment faces challenges due to distribution shifts and evolving test samples. Personalized Federated Learning (PFL) tailors the global model to individual client distributions, but struggles with Out-Of-Distribution (OOD) samples during testing, leading to performance degradation. In real-world scenarios, balancing personalization and generalization during online testing is crucial and existing methods primarily focus on training-phase generalization. To address the test-time trade-off, we introduce a new scenario: Test-time Generalization for Internal and External Distributions in Federated Learning (TGFL), which evaluates adaptability under Internal Distribution (IND) and External Distribution (EXD). We propose BTFL, a Bayesian-based test-time generalization method for TGFL, which balances generalization and personalization at the sample level during testing. BTFL employs a two-head architecture to store local and global knowledge, interpolating predictions via a dual-Bayesian framework that considers both historical test data and current sample characteristics with theoretical guarantee and faster speed. Our experiments demonstrate that BTFL achieves improved performance across various datasets and models with less time cost. The source codes are made publicly available at https://github.com/ZhouYuCS/BTFL.
CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables
Authors: Pengfei Zhou, Yunlong Liu, Junli Liang, Qi Song, Xiangyang Li
Venue: KDD
Year: 2025

Time series forecasting with exogenous variables is a critical emerging paradigm that presents unique challenges in modeling dependencies between variables. Traditional models often struggle to differentiate between endogenous and exogenous variables, leading to inefficiencies and overfitting. In this paper, we introduce CrossLinear, a novel Linear-based forecasting model that addresses these challenges by incorporating a plug-and-play cross-correlation embedding module. This lightweight module captures the dependencies between variables with minimal computational cost and seamlessly integrates into existing neural networks. Specifically, it captures time-invariant and direct variable dependencies while disregarding time-varying or indirect dependencies, thereby mitigating the risk of overfitting in dependency modeling and contributing to consistent performance improvements. Furthermore, CrossLinear employs patch-wise processing and a global linear head to effectively capture both short-term and long-term temporal dependencies, further improving its forecasting precision. Extensive experiments on 12 real-world datasets demonstrate that CrossLinear achieves superior performance in both short-term and long-term forecasting tasks. The ablation study underscores the effectiveness of the cross-correlation embedding module. Additionally, the generalizability of this module makes it a valuable plug-in for various forecasting tasks across different domains. Codes are available at https://github.com/mumiao2000/CrossLinear.
Grid and Road Expressions Are Complementary for Trajectory Representation Learning
Authors: Silin Zhou, Shuo Shang, Lisi Chen, Peng Han, Christian S. Jensen
Venue: KDD
Year: 2025

Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement in a road network, as input. We observe that the two types of trajectories are complementary, providing either region and location information or providing road structure and movement regularity. Therefore, we propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning. In particular, we transform raw GPS trajectories into both grid and road trajectories and tailor two encoders to capture their respective information. To align the two encoders such that they complement each other, we adopt a contrastive loss to encourage them to produce similar embeddings for the same raw trajectory and design a mask language model (MLM) loss to use grid trajectories to help reconstruct masked road trajectories. To learn the final trajectory representation, a dual-modal interactor is used to fuse the outputs of the two encoders via cross-attention. We compare GREEN with 7 state-of-the-art TRL methods for 3 downstream tasks, finding that GREEN consistently outperforms all baselines and improves the accuracy of the best-performing baseline by an average of 15.99%. Code and data are available at https://github.com/slzhou-xy/GREEN.