Regular papers 常规论文
| Paper ID 论文编号 | Title 标题 | Author Names 作者姓名 | Keywords 关键词 |
|---|---|---|---|
| DM211 | Generating Realistic Tabular Data with Large Language Model 生成具有大型语言模型的逼真表格数据 |
Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, and Svetha Venkatesh | Classification, Generative model, Large language model (LLM), Tabular data generation, Realistic tabular samples 分类，生成模型，大型语言模型（LLM），表格数据生成，逼真的表格样本 |
| DM216 | Graph Community Augmentation with GMM-based Modeling in Latent Space 图社区增强：基于潜在空间 GMM 建模 |
Shintaro Fukushima and Kenji Yamanishi | Community Augmentation, Extrapolation, Generative Model, Graph Generation, Minimum Description Length Principle 社区增强，外推，生成模型，图生成，最小描述长度原理 |
| DM233 | Solving Combinatorial Optimization Problem over Graph through QUBO Transformation and Deep Reinforcement Learning 解决通过 QUBO 转换和深度强化学习在图上的组合优化问题 |
Tianle Pu, Chao Chen, Li Zeng, Shixuan Liu, Rui Sun, and Changjun Fan | Combinatorial Optimization, Quadratic Unconstrained Binary Optimization, Reinforcement Learning, Graph Transformer 组合优化，二次无约束二进制优化，强化学习，图变换 |
| DM245 | HyperTime: A Dynamic Hypergraph Approach for Time Series Classification 超时：一种用于时间序列分类的动态超图方法 |
Raneen Younis and Zahra Ahmadi | Time series classification, Dynamic hypergraph, Graph neural networks 时间序列分类，动态超图，图神经网络 |
| DM254 | Towards Efficient Ridesharing via Order-Vehicle Pre-Matching Using Attention Mechanism 朝着通过使用注意力机制进行订单-车辆预匹配以提高拼车效率的方向 |
Zhidan Liu, Jinye Lin, Zhiyu Xia, Chao Chen, and Kaishun Wu | Ridesharing, Order-vehicle pre-matching, Self-attention mechanism, Spatial-temporal 拼车，订单车辆预匹配，自注意力机制，时空 |
| DM270 | DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning DFDG：无数据双生成器对抗蒸馏用于单次联邦学习 |
Kangyang Luo, Shuai Wang, Yexuan Fu, Renrong Shao, Xiang Li, Yunshi Lan, Ming Gao, and Jinlong Shu | One-shot Federated Learning, Data-free knowledge distillation, Data heterogeneity, Model heterogeneity 一次性联邦学习，无数据知识蒸馏，数据异构性，模型异构性 |
| DM277 | Transitivity-Encoded Graph Attention Networks for Complementary Item Recommendations 互补物品推荐的传递性编码图注意力网络 |
Jin Shang, Yang Jiao, Chenghuan Guo, Minghao Sun, Yan Gao, Jia Liu, Michinari Momma, Itetsu Taru, and Yi Sun | complementary recommendation, graph neural networks, graph attention networks, self-supervised learning 互补推荐，图神经网络，图注意力网络，自监督学习 |
| DM288 | SR-PredictAO: Session-based Recommendation with High-Capability Predictor Add-On SR-PredictAO：基于会话的推荐与高能力预测器附加组件 |
Ruida WANG, Raymond Chi-Wing Wong, and Weile TAN | session-based recommendation, recommender system, neural decision forest, tree-based method 基于会话的推荐，推荐系统，神经决策森林，基于树的算法 |
| DM301 | Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder 提升时序编码的噪声感知自监督学习和高效编码器 |
Duy Nguyen Anh, Trang Tran, Hieu Pham Huy, Le Nguyen Phi, and Lam Nguyen Minh | Time series representation learning, Noise-resiliency training strategy, Inception 时间序列表示学习，噪声鲁棒性训练策略，Inception |
| DM306 | Efficient Network Embedding by Approximate Equitable Partitions 高效近似等分网络嵌入 |
Giuseppe Squillace, Mirco Tribastone, Max Tschaikowski, and Andrea Vandin | Equitable partitions, network embedding, backward equivalence, structural equivalence 公平划分，网络嵌入，逆向等价，结构等价 |
| DM315 | Hierarchical Explanations for Text Classification Models: Fast and Effective 文本分类模型的层次化解释：快速且有效 |
Zhenyu Nie, Zheng Xiao, Huizhang Luo, Xuan Liu, and Anthony Theodore Chronopoulos | Hierarchical interpretation, Explanation efficiency, Word interactions, Text classification 层次解释，解释效率，词语交互，文本分类 |
| DM319 | ADOD: Adaptive Density Outlier Detection ADOD：自适应密度异常检测 |
Li Qian, Jing Qian, Xin Sun, Wengang Guo, and Christian Böhm | Outlier Detection, Unsupervised Learning, Adaptive Density, Mutual Neighbors Graph 异常检测，无监督学习，自适应密度，互邻接图 |
| DM322 | Adaptive Graph Neural Networks for Cold-start Multimedia Recommendation 自适应图神经网络在冷启动多媒体推荐中的应用 |
Zhen Li, Jibin Wang, Zhuo Chen, Kun Wu, Yuanzhen Wei, and Hai Huang | Multimedia Recommendation, Graph Neural Network, Social Network, Multimodal 多媒体推荐，图神经网络，社交网络，多模态 |
| DM323 | Graph Contrastive Learning with Adversarial Structure Refinement (GCL-ASR) 图对比学习与对抗结构细化（GCL-ASR） |
Jiangwen Chen, Kou Guang, Qiyang Li, and Tan Hao | GNN, GCL, GANs, Data mining GNN, GCL, GANs，数据挖掘 |
| DM327 | Adaptive Loss-ware Modulation for Multimedia Retrieval 自适应损失调制用于多媒体检索 |
Jian Zhu, Yu Cui, Zeyi Sun, Yuyang Dai, Xi Wang, Lei Liu, Cheng Luo, and Li-Rong Dai | Multi-view Hash, Gradient Modulation, Multi-modal Hash, Multimedia Retrieval, Image Retrieval. 多视图哈希，梯度调制，多模态哈希，多媒体检索，图像检索。 |
| DM331 | Enhancing Embeddings Quality with Stacked Gate for Click-Through Rate Prediction 提升点击率预测中嵌入质量通过堆叠门控 |
Caihong Mu, Yunfei Fang, Jialiang Zhou, and Yi Liu | CTR Prediction, AutoML, Neural Architecture Search, Recommendation CTR 预测，AutoML，神经架构搜索，推荐 |
| DM337 | Towards Cross-domain Few-shot Graph Anomaly Detection 跨领域小样本图异常检测 |
Jiazhen Chen, Sichao Fu, Zhibin Zhang, Zheng Ma, Mingbin Feng, Tony Wirjanto, and Qinmu Peng | Graph Anomaly Detection, Graph Neural Network, Few-shot Learning, Domain Adaptation, Prompt Learning 图异常检测，图神经网络，小样本学习，领域自适应，提示学习 |
| DM359 | Debunking Fake News in Online Social Networks without Text Analysis 揭露在线社交网络中不实新闻，无需文本分析 |
Xing Su, Jian Yang, Jia Wu, and Zitai Qiu | Fake News Detection, Hypergraph, High-order Relations, Text-independent 虚假新闻检测，超图，高阶关系，文本无关 |
| DM363 | Scalable Order-Preserving Pattern Mining 可扩展的有序保持模式挖掘 |
Ling Li, Wiktor Zuba, Grigorios Loukides, Solon Pissis, and Maria Matsangidou | frequent pattern mining, order-preserving, string algorithms, indexing 频繁模式挖掘、有序保持、字符串算法、索引 |
| DM366 | Designing an attack-defense game: how to increase the robustness of financial transaction models via a competition 设计攻防游戏：如何通过竞赛提高金融交易模型的鲁棒性 |
Alexey Zaytsev, Alex Natekin, Evgeni Vorsin, Valerii Smirnov, Georgii Smirnov, Oleg Sidorshin, Alexander Senin, Alexander Dudin, Maria Kovaleva, and Dmitry Berestnev | Adversarial attacks, Robustness, Deep learning, Financial data 对抗攻击、鲁棒性、深度学习、金融数据 |
| DM367 | Continuous Exact Explanations of Neural Networks 持续精确的神经网络解释 |
Alice Dethise and Marco Canini | explainable ML, machine learning, post-hoc explanations 可解释机器学习，机器学习，事后解释 |
| DM373 | Utilitarian Online Learning from Open-World Soft Sensing 功利主义开放世界软传感在线学习 |
Heng Lian, Yu Huang, Xingquan Zhu, and Yi He | Data Streams, Online Learning, Optimal Rejection, Soft Sensing, Industrial Data Mining 数据流，在线学习，最优拒绝，软测量，工业数据挖掘 |
| DM378 | Probabilistic Matrix Factorization-based Three-stage Label Completion for Crowdsourcing 基于概率矩阵分解的三阶段众包标签补全 |
Boyi Yang, Liangxiao Jiang, and Wenjun Zhang | Crowdsourcing learning, label completion, probabilistic matrix factorization 众包学习，标签完成，概率矩阵分解 |
| DM383 | Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs 动态图中的信息子图感知掩码自编码器 |
Pengfei Jiao, Xinxun Zhang, Mengzhou Gao, and Tianpeng Li | graph representation learning, dynamic graphs, masked auto-encoder, self-supervised learning 图表示学习，动态图，掩码自动编码器，自监督学习 |
| DM388 | ELiCiT: Effective and Lightweight Lossy Compression of Tensors ELiCiT：有效且轻量级的张量有损压缩 |
Jihoon Ko, Taehyung Kwon, Jinhong Jung, and Kijung Shin | Matrix Compression, Tensor Compression, Matrix Completion, Neural-network Compression 矩阵压缩，张量压缩，矩阵补全，神经网络压缩 |
| DM393 | LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data LISA：异构时空数据上交通事故预测的学习集成空间划分框架 |
Bang An, Xun Zhou, Amin Khezerlou, Nick Street, Jinping Guan, and Jun Luo |
Spatialtemporal Data Mining, Traffic Accident Forecasting 时空数据挖掘，交通事故预测 |
| DM402 | RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion RecCoder：将序列推荐重新表述为基于大型语言模型的代码补全 |
Kai-Huang Lai, Wudong Xi, Xingxing Xing, Wei Wan, Chang-Dong Wang, Min Chen, and Mohsen Guizani | large language model, sequential recommendation 大型语言模型，顺序推荐 |
| DM409 | Scaling Disk Failure Prediction via Multi-Source Stream Mining 通过多源流挖掘扩展磁盘故障预测 |
Shujie Han, Zirui Ou, Qun Huang, and Patrick P. C. Lee | disk failure prediction, multi-source stream mining, scalability 磁盘故障预测，多源流挖掘，可扩展性 |
| DM410 | Contrastive Learning for Adapting Language Model to Sequential Recommendation 对比学习以适应语言模型到序列推荐 |
Fei-Yao Liang, Wu-Dong Xi, Xing-Xing Xing, Wei Wan, Chang-Dong Wang, Min Chen, and Mohsen Guizani | sequential recommendation, contrastive learning, large language model 顺序推荐，对比学习，大型语言模型 |
| DM412 | GQ*: Towards Generalizable Deep Q-Learning for Steiner Tree in Graphs GQ*: 图中 Steiner 树的可泛化深度 Q 学习 |
Wei Huang, Hanchen Wang, Dong Wen, Xuefeng Chen, Wenjie zhang, and Ying Zhang | Steiner Tree, Reinforcement Learning, Graph Neural Networks, A* Search Steiner 树，强化学习，图神经网络，A*搜索 |
| DM413 | HomoMGC: Homophily-enhanced Adaptive Graph Refinement for Multi-view Graph Clustering HomoMGC：基于同质性的自适应图细化多视图图聚类 |
Man-Sheng Chen, Xiao-Sha Cai, Chang-Dong Wang, Dong Huang, Min Chen, and Mohsen Guizani | multi-view graph clustering, homophily assumption, heterogeneous edges, adaptive graph refinement, low-rank tensor 多视图图聚类，同质假设，异构边，自适应图细化，低秩张量 |
| DM419 | Cross-Store Next-Basket Recommendation 跨店下一购物篮推荐 |
Liangchen Ma, Ya Li, Zifeng Mai, Feiyao Liang, Chang-Dong Wang, Min Chen, and Mohsen Guizani | next-basket recommendation, cross-domain recommendation, graph neural network 下一篮推荐，跨域推荐，图神经网络 |
| DM430 | Emotional Synchronization for Audio-Driven Talking-Head Generation 情感同步驱动的头部说话生成 |
Zhao Zhang, Yan Luo, Zhichao Zuo, Richang Hong, Yi Yang, and Meng Wang | Audio-driven talking-head synthesis, Emotional synchronization, Attention 音频驱动的人物头像合成，情感同步，注意力 |
| DM436 | High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Video Editing 高保真零样本文本引导视频编辑扩散编辑器 |
Yan Luo, Zhichao Zuo, Zhao Zhang, Zhongqiu Zhao, Haijun Zhang, and Richang Hong | Zero-shot video editing, High-fidelity, Diffusion-based generative model, Text-to-video, Spatial-temporal attention. 零样本视频编辑，高保真，基于扩散的生成模型，文本到视频，时空注意力。 |
| DM438 | Early Fire Detection based on Local Morphological Knowledge Matching 基于局部形态知识匹配的早期火灾探测 |
Xinzhi Wang, Mengyue Li, Nengjun Zhu, Jiayan Qian, and Zhanyi Zheng | local morphological knowledge, fire object localization, early fire detection 本地形态学知识，火灾目标定位，早期火灾探测 |
| DM442 | GADIN: Generative Adversarial Denoise Imputation Network for Incomplete Data GADIN：用于不完整数据的生成对抗去噪插补网络 |
Dong Li, Zhicong Liu, Mingfeng Hu, Baoyan Song, and Xiaohuan Shan | Missing data, Data imputation, Generative adversarial network, Denoising network 缺失数据，数据插补，生成对抗网络，去噪网络 |
| DM455 | APOLLO: Differential Private Online Multi-Sensor Data Prediction with Certified Performance 阿波罗：具有认证性能的差分隐私在线多传感器数据预测 |
Honghui Xu, Wei Li, Shaoen Wu, Liang Zhao, and Zhipeng Cai | Multi-sensor Data Analysis, Differential Privacy, Correlated Data Privacy 多传感器数据分析，差分隐私，相关数据隐私 |
| DM461 | Combining Self-Supervision and Privileged Information for Representation Learning from Tabular Data 结合自监督和特权信息进行表格数据表示学习 |
Haoyu Yang, Gyorgy Simon, Michael Steinbach, Genevieve Melton, and Vipin Kumar | Self-Supervised Learning, Privileged Information, Representation Learning, Healthcare 自监督学习，特权信息，表示学习，医疗保健 |
| DM475 | Align Along Time and Space: A Graph Latent Diffusion Model for Traffic Dynamics Prediction 时间与空间对齐：用于交通动态预测的图隐扩散模型 |
Yuhang Liu, Yingxue Zhang, Xin Zhang, Yu Yang, Yiqun Xie, Sahar Ghanipoor Machiani, Yanhua Li, and Jun Luo | urban dynamics prediction, latent diffusion models, spatial-temporal data mining 城市动态预测，潜在扩散模型，时空数据挖掘 |
| DM482 | EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection EEiF：具有 e 分支的高效孤立森林异常检测 |
Yifan Zhang, Haolong Xiang, Xuyun Zhang, Xiaolong Xu, Wei Fan, Qin Zhang, and Lianyong Qi | Anomaly Detection, FR clustering, Efficient, Parallel Algorithm 异常检测，FR 聚类，高效，并行算法 |
| DM488 | Margin-bounded Confidence Scores for Out-of-Distribution Detection 分布外检测的边界置信度得分 |
Lakpa Tamang, Mohamed Reda Bouadjenek, Richard Dazeley, and Sunil Aryal | out-of-distribution, outlier exposure, confidence score, weighted penalty 分布外，异常值暴露，置信度得分，加权惩罚 |
| DM510 | Towards Dynamic University Course Timetabling Problem: An Automated Approach Augmented via Reinforcement Learning 朝向动态大学课程排课问题：通过强化学习增强的自动化方法 |
Yanan Xiao, XiangLin Li, Lu Jiang, Pengfei Wang, Kaidi Wang, and Na Luo | Course Timetable, Reinforcement Learning 课程时间表，强化学习 |
| DM515 | Fast and Accurate Triangle Counting in Graph Streams Using Predictions 快速准确的图流中三角形计数使用预测 |
Cristian Boldrin and Fabio Vandin | Triangle Counting, Sampling, Algorithms with Predictions, Graph Stream Mining 三角形计数、采样、预测算法、图流挖掘 |
| DM546 | Efficiently Manipulating Structural Graph Clustering Under Jaccard Similarity 高效地基于 Jaccard 相似度进行结构图聚类操作 |
Chuanyu Zong, Rui Fang, Meng-xiang Wang, Tao Qiu, and Anzhen Zhang | Structural graph clustering, Jaccard similarity, Manipulation, Incremental computation 结构图聚类，Jaccard 相似度，操作，增量计算 |
| DM591 | HFGNN: Efficient Graph Neural Networks using Hub-Fringe Structures HFGNN：使用中心-边缘结构的有效图神经网络 |
Pak Lon Ip, Sheng Hui Zhang, Xue Kai Wei, Tsz Nam Chan, and Leong Hou U | Expressivity, Graph Neural Networks, Hub-Fringe, Hub Labeling 表达性、图神经网络、中心-边缘、中心标记 |
| DM610 | A Bayesian Hierarchical Model for Orthogonal Tucker Decomposition with Oblivious Tensor Compression 贝叶斯层次模型用于正交 Tucker 分解与无意识张量压缩 |
Matthew Pietrosanu, Bei Jiang, and Linglong Kong | Bayes methods, Compressed sensing, Monte Carlo methods, Statistical analysis, Tensors 贝叶斯方法，压缩感知，蒙特卡洛方法，统计分析，张量 |
| DM611 | Normalizing self-supervised learning for provably reliable Change Point Detection 标准化自监督学习以实现可证明可靠的变化点检测 |
Alexandra Bazarova, Evgenia Romanenkova, and Alexey Zaytsev | change point detection, self-supervised learning, spectral normalization 变化点检测，自监督学习，谱归一化 |
| DM634 | Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive Learning for Disorder Analysis 反事实脑图增强引导的双层对比学习用于疾病分析 |
Guangwei Dong, Xuexiong Luo, Jing Du, Jia Wu, Shan Xue, Jian Yang, and Amin Beheshti | Counterfactual augmentation, Brain disorder analysis, Graph contrastive learning 反事实增强，脑部疾病分析，图对比学习 |
| DM641 | CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification CounterFair：用于偏差检测、缓解和子群体识别的分组反事实 |
Alejandro Kuratomi, Zed Lee, Panayiotis Tsaparas, Guilherme Dinis Junior, Evaggelia Pitoura, Tony Lindgren, and Panagiotis Papapetrou | Counterfactual explanations, Algorithmic fairness, Group counterfactuals, Local explainability 反事实解释、算法公平性、群体反事实、局部可解释性 |
| DM655 | Financial Risk Assessment via Long-term Payment Behavior Sequence Folding 通过长期支付行为序列折叠进行财务风险评估 |
Yiran Qiao, Yateng Tang, Xiang Ao, Qi Yuan, Ziming Liu, Chen Shen, and Xuehao Zheng | Financial Risk Assessment, Long Sequence Modeling, User Behavior Modeling 金融风险评估，长序列建模，用户行为建模 |
| DM667 | Scalable Graph Classification via Random Walk Fingerprints 可扩展的图分类通过随机游走指纹 |
Peiyan Li, Honglian Wang, and Christian Böhm | Graph Classification, Feature Extraction, Scalability, Interpretability 图分类，特征提取，可扩展性，可解释性 |
| DM690 | Dual Cross-Stage Partial Learning for Enhanced Object Detection in Dehazed Images 双阶段部分学习以增强去雾图像中的目标检测 |
Jinbiao Zhao, Zhao Zhang, Jiahuan Ren, Haijun Zhang, Zhongqiu Zhao, and Meng Wang | Image dehazing, object detection, anchor-free, dual cross stage partial learning 图像去雾，目标检测，无锚点，双交叉阶段部分学习 |
| DM697 | Resource2Box: Learning To Rank Resources in Distributed Search Using Box Embedding 资源 2 盒：使用 Box Embedding 在分布式搜索中学习排序资源 |
Ulugbek Ergashev, Geon Lee, Kijung Shin, Eduard Dragut, and Weiyi Meng | Box Embedding, Learning to Rank, Distributed Search 盒子嵌入，学习排序，分布式搜索 |
| DM709 | ChronoCTI: Mining Knowledge Graph of Temporal Relations among Cyberattack Actions ChronoCTI：挖掘网络攻击行为之间时间关系知识图谱 |
Md Rayhanur Rahman, Brandon Wroblewski, Quinn Matthews, Brantley Morgan, Timothy Menzies, and Laurie Williams | MITRE ATT&CK, Temporal relation, Cyberthreat intelligence, CTI reports, Knowledge graph MITRE ATT&CK, 时间关系，网络威胁情报，CTI 报告，知识图谱 |
| DM713 | Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 联邦交通流量预测中的个性化交通模式共享 |
Hang Zhou, Wentao Yu, Sheng Wan, Yongxin Tong, Tianlong Gu, and Chen Gong | spatial-temporal data, traffic flow prediction, personalized federated learning 空间-时间数据，交通流量预测，个性化联邦学习 |
| DM717 | Warm-Starting Contextual Bandits under Latent Reward Scaling 暖启动下的潜在奖励缩放上下文 Bandits |
Bastian Oetomo, R. Malinga Perera, Renata Borovica-Gajic, and Benjamin I. P. Rubinstein | multi-armed bandits, warm-start, pre-training 多臂老虎机，热启动，预训练 |
| DM734 | Feature Map Purification for Enhancing Adversarial Robustness of Deep Timeseries Classifiers 特征图净化以增强深度时间序列分类器的对抗鲁棒性 |
Mubarak Abdu-Aguye, Zaigham Zaheer, and Karthik Nandakumar | adversarial robustness, feature maps, purification, timeseries, wavelets 对抗鲁棒性、特征图、净化、时间序列、小波 |
| DM743 | Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations 自适应过程引导学习：在预测湖泊溶解氧浓度中的应用 |
Runlong Yu, Chonghao Qiu, Robert Ladwig, Paul Hanson, Yiqun Xie, Yanhua Li, and Xiaowei Jia | physics-guided learning, knowledge integration, adaptive learning, ecosystem modeling 物理引导学习，知识整合，自适应学习，生态系统建模 |
| DM745 | TROPICAL: Transformer-based Hypergraph Learning for Camouflaged Fraudsters Detection 热带：基于 Transformer 的超图学习用于伪装欺诈者检测 |
Venus Haghighi, Behnaz Soltani, Nasrin Shabani, Jia Wu, Yang Zhang, Lina Yao, Quan Z. Sheng, and Jian Yang | Hypergraph Learning, Camouflage, Fraud Detection 超图学习，伪装，欺诈检测 |
| DM747 | DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation DISCO：一种可解释的职位推荐分层解耦认知诊断框架 |
Xiaoshan Yu, Chuan Qin, Qi Zhang, Chen Zhu, Haiping Ma, Xingyi Zhang, and Hengshu Zhu | Online recruitment, job recommendation, cognitive diagnosis, disentangled learning 在线招聘，职位推荐，认知诊断，解耦学习 |
| DM760 | MOStream: A Modular and Self-Optimizing Data Stream Clustering Algorithm MOStream：一种模块化和自优化的数据流聚类算法 |
Zhengru Wang, Xin Wang, and Shuhao Zhang | data stream clustering, cluster evolution, outlier evolution, dimention changes 数据流聚类，聚类演化，异常值演化，维度变化 |
| DM772 | TAN: A Tripartite Alignment Network Enhancing Composed Image Retrieval with Momentum Distillation TAN：一种增强组合图像检索的动量蒸馏三路对齐网络 |
Yongquan Wan, Erhe Yang, Cairong Yan, Guobing Zou, and Bofeng Zhang | image retrieval, composed image retrieval, multimodal, knowledge distillation 图像检索，合成图像检索，多模态，知识蒸馏 |
| DM776 | PROMIPL:A Probabilistic Generative Model for Multi-Instance Partial-Label Learning PROMIPL：多实例部分标签学习的概率生成模型 |
Yin-Fang Yang, Wei Tang, and Min-Ling Zhang | Multi-Instance Partial-Label Learning, Generative Model, Probabilistic Disambiguation, Label Distribution, Variational Bayesian. 多实例部分标签学习，生成模型，概率消歧，标签分布，变分贝叶斯。 |
| DM778 | Bi-level User Modeling for Deep Recommender Systems 双层用户建模用于深度推荐系统 |
Yejing Wang, Dong Xu, Xiangyu Zhao, Zhiren Mao, Peng Xiang, Ling Yan, Yao Hu, Zijian Zhang, Xuetao Wei, and Qidong Liu | User Modeling, Deep Recommender Systems, CTR Prediction 用户建模，深度推荐系统，点击率预测 |
| DM783 | A Novel Shadow Variable Catcher for Addressing Selection Bias in Recommendation Systems 一种用于解决推荐系统中选择偏差的新型阴影变量捕获器 |
Qingfeng Chen, Boquan Wei, Debo Cheng, Jiuyong Li, Lin Liu, and Shichao Zhang | Recommendation systems, causal inference, selection bias, shadow variables. 推荐系统、因果推断、选择偏差、影子变量。 |
| DM790 | EMIT - Event Based Masked Auto Encoding for Irregular Time Series EMIT - 基于事件的掩码自动编码用于不规则时间序列 |
Hrishikesh Patel, Ruihong Qiu, Adam Irwin, Shazia Sadiq, and Sen Wang | Irregular time series, Self-supervised learning, Healthcare 不规律时间序列，自监督学习，医疗保健 |
| DM806 | A Learned Approach to Index Algorithm Selection 学习型索引算法选择方法 |
Chaohong Ma, Xiaohui Yu, Yifan Li, Aishan Maoliniyazi, and Xiaofeng Meng 茅超红，余晓辉，李一帆，马爱山，孟晓峰 |
Algorithm selection, Featurization, Adaptation 算法选择、特征化、适应 |
Short papers 简短论文
| Paper ID 论文编号 | Title 标题 | Author Names 作者姓名 | Keywords 关键词 |
|---|---|---|---|
| DM223 | MetaSTC: A Meta Spatio-Temporal Learning Paradigm for Traffic Flow Prediction MetaSTC：一种用于交通流量预测的元时空学习范式 |
Kexin Xu, Zhemeng Yu, Yucen Gao, Songjian Zhang, Jun Fang, Xiaofeng Gao, and Guihai Chen | Spatio-Temporal Data Mining, Meta-Learning, Traffic Flow Prediction, Backbone Agnostic 时空数据挖掘，元学习，交通流量预测，无骨干网络 |
| DM227 | Matrix Profile for Anomaly Detection on Multidimensional Time Series 矩阵轮廓在多维时间序列异常检测中的应用 |
Chin-Chia Michael Yeh, Audrey Der, Uday Singh Saini, Vivian Lai, Yan Zheng, Junpeng Wang, Xin Dai, Zhongfang Zhuang, Yujie Fan, Huiyuan Chen, Prince Aboagye, Liang Wang, Wei Zhang, and Eamonn Keogh | time series, anomaly detection, multidimensionality 时间序列，异常检测，多维性 |
| DM241 | Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce Search 嗨-高能：大规模个性化电子商务搜索的生成式检索 |
YanjingWu Wu, Yinfu Feng, Jian Wang, Wenji Zhou, Yunan Ye, Rong Xiao, and Jun Xiao | Search and Recommendation System, Information Retrieval, Generative Retrieval, Personalization 搜索与推荐系统，信息检索，生成式检索，个性化 |
| DM259 | 2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables 2DXformer：具有双重外生变量的风力发电预测的双向 Transformer |
Yajuan Zhang, Jiahai Jiang, Yule Yan, liang Yang, and ping zhang | wind power forecasting, spatiotemporal forecasting, exogenous variables, variable correlation 风力发电预测，时空预测，外生变量，变量相关性 |
| DM266 | Goal-guided Generative Prompt Injection Attack on Large Language Models 目标引导的大语言模型生成式提示注入攻击 |
Chong Zhang, Mingyu Jin, Qinkai Yu, Chengzhi Liu, Haochen Xue, and Xiaobo Jin | Prompt Injection, KL-divergence, Robustness, Mahalanobis Distance 提示注入，KL 散度，鲁棒性，马氏距离 |
| DM271 | CL4CO: A Curriculum Training Framework for Graph-based Neural Combinatorial Optimization CL4CO：基于图神经组合优化的课程培训框架 |
Yang Liu, Chuan Zhou, Peng Zhang, Zhao Li, Shuai Zhang, Xixun Lin, and Xindong Wu | curriculum learning, combinatorial optimization, graph neural networks 课程学习，组合优化，图神经网络 |
| DM295 | QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations QUCE：基于路径的不确定性最小化和量化，用于生成反事实解释 |
Jamie Duell, Monika Seisenberger, Hsuan Fu, and Xiuyi Fan | Counterfactuals, Explainability, Deep Learning, Neural Networks, Interpretability 反事实，可解释性，深度学习，神经网络，可解释性 |
| DM320 | A Momentum Contrastive Learning Framework for Query-POI Matching 一个用于查询-POI 匹配的动量对比学习框架 |
Yuting Qiang, Jianbin Zheng, Lixia Wu, Haomin Wen, Junhong Lou, and Minhui Deng | cross-modal learning, contrastive learning, query-POI matching 跨模态学习，对比学习，查询-POI 匹配 |
| DM325 | Generalized Sparse Additive Model with Unknown Link Function 广义未知链接函数稀疏加性模型 |
Peipei Yuan, Xinge You, Hong Chen, Xuelin Zhang, and Qinmu Peng | generalized additive models, unknown link function, variable interaction, bilevel optimization, convergence analysis 广义加性模型，未知连接函数，变量交互作用，双层优化，收敛性分析 |
| DM326 | SHADE: Deep Density-based Clustering 阴影：基于深度密度的聚类 |
Anna Beer, Pascal Weber, Lukas Miklautz, Collin Leiber, Walid Durani, Christian Böhm, and Claudia Plant | Clustering, Deep Clustering, Density-based Clustering, DBSCAN 聚类，深度聚类，基于密度的聚类，DBSCAN |
| DM334 | Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection 相互依赖至关重要：多元时间序列异常检测的图对齐 |
Yuanyi Wang, Haifeng Sun, Chengsen Wang, Mengde Zhu, Wei Tang, Jingyu Wang, Qi Qi, Zirui Zhuang, and Jianxin Liao | multivariate time series, anomaly detection, graph alignment, unsupervised learning 多元时间序列，异常检测，图对齐，无监督学习 |
| DM343 | Exploitation or Exploration Next? User Behavior Decoupling and Emerging Intent Modeling for Next-Item Recommendation 利用还是探索？用户行为解耦与新兴意图建模用于下一项推荐 |
Nengjun Zhu, Lingdan Sun, Xiangfeng Luo, Jian Cao, Qi Zhang, and Xinjiang Lu | Session-based recommendation, Intent modeling, Behavior decoupling, Hypergraph learning, Neighbor retrieval 会话式推荐，意图建模，行为解耦，超图学习，邻居检索 |
| DM371 | Multi-modal Sarcasm Detection via Dual Synergetic Perception Graph Convolutional Networks 多模态讽刺检测通过双协同感知图卷积网络 |
Xingjie Zhuang and Zhixin Li | Sarcasm detection, Multi-modal learning, Information fusion, Graph networks, Knowledge embedding 讽刺检测，多模态学习，信息融合，图网络，知识嵌入 |
| DM384 | Exploratory Combinatorial Optimization Problem Solving via Gauge Transformation 探索性组合优化问题求解通过规范变换 |
Tianle Pu, Changjun Fan, Mutian Shen, Yizhou Lu, Li Zeng, Zohar Nussinov, Chao Chen, and Zhong Liu | Combinatorial Optimization, Gauge Transformation, Graph Neural Network, Reinforcement Learning, MaxCut Problem 组合优化、规范变换、图神经网络、强化学习、最大切割问题 |
| DM385 | SplitSEE: A Splittable Self-supervised Framework for Single-channel EEG Representation Learning SplitSEE：一种可分割的自监督单通道脑电图表示学习方法 |
Rikuto Kotoge, Zheng Chen, Tasuku Kimura, Yasuko Matsubara, Takufumi Yanagisawa, Haruhiko Kishima, and Yasushi Sakurai | EEG, representation learning, self-supervised learning, federated learning 脑电图，表示学习，自监督学习，联邦学习 |
| DM390 | Accurate and Fast Estimation of Temporal Motifs using Path Sampling 精确快速地使用路径采样估计时间模式 |
Yunjie Pan, Omkar Bhalerao, C. Seshadhri, and Nishil Talati | temporal graphs, temporal motif mining, approximate algorithms 时间图，时间基序挖掘，近似算法 |
| DM394 | DynoGraph: Dynamic Graph Construction for Nonlinear Dimensionality Reduction DynoGraph：非线性降维的动态图构建 |
Li Qian, Claudia Plant, Yalan Qin, Jing Qian, and Christian Böhm | Dimensionality Reduction, Unsupervised Learning, Adaptive Neighborhood Graph, Dynamic Graph Modification 维度降低，无监督学习，自适应邻域图，动态图修改 |
| DM414 | Periodic Prompt on Dynamic Heterogeneous Graph for Next Basket Recommendation 周期性动态异构图在下一篮子推荐中的应用提示 |
Ru-Bin Li, Man-Sheng Chen, Xin-Yu Ding, Chang-Dong Wang, Sihong Xie, Shuangyin Liu, Min Chen, and Mohsen Guizani | graph prompt, graph neural network, next basket recommendation, dynamic and heterogeneous information 图提示，图神经网络，下一篮推荐，动态异构信息 |
| DM446 | Constructing $\epsilon$-Constrained Sparsified $\beta^s$-Complexes using Space Partitioning Trees 构建使用空间划分树进行稀疏化的 $\epsilon$-约束 $\beta^s$-复形 |
Rohit Singh and Philip Wilsey | Sparsification, Space Partitioning, Persistent Homology, Data Mining 稀疏化、空间划分、持久同伦、数据挖掘 |
| DM449 | Channel-Attentive Graph Neural Networks 通道注意力图神经网络 |
TuÄŸrul Hasan Karabulut and Ä°nci M. BaytaÅŸ | Deep learning, Graph neural network, Representation learning, Attention 深度学习，图神经网络，表示学习，注意力 |
| DM454 | A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models 一种用于零样本链接预测的大语言模型的压缩转换图框架 |
Mingchen Li, Chen Ling, rui Zhang, and Liang Zhao | Zero-Shot Link Prediction, Condensed Transition, Graph, Large Language Models 零样本链接预测，压缩转换，图，大型语言模型 |
| DM462 | Towards Expressive Graph Representations for Graph Neural Networks 朝着图神经网络的表达性图表示迈进 |
Chengsheng Mao, Liang Yao, and Yuan Luo | graph neural network, graph representation, expressive power, injective mapping, set representation. 图神经网络，图表示，表达能力，注入映射，集合表示。 |
| DM467 | Weakly-Supervised Graph Classification with Even a Single Key Subgraph Per Class 弱监督图分类，即使每个类别只有一个关键子图 |
Lu Zhang, Chenbo Zhang, Jihong Guan, and Shuigeng Zhou | graph classification, weakly-supervised learning, subgraphs 图分类，弱监督学习，子图 |
| DM483 | Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network 期货异构持续图神经网络量化投资 |
Zhizhong Tan, Min Hu, Bin Liu, and Guosheng Yin | Continual learning, futures price forecasting, graph neural network, spatio-temporal data 持续学习，期货价格预测，图神经网络，时空数据 |
| DM495 | Influence-aware Group Recommendation for Social Media Propagation 感知影响力的社交媒体传播群组推荐 |
Chengkun He, Xiangmin Zhou, Chen Wang, Longbing Cao, Jie Shao, and Zahir Tari | group recommendation, influence propagation 群组推荐，影响传播 |
| DM497 | Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network 多双曲空间异构图注意力网络 |
Jongmin Park, Seunghoon Han, Jong-Ryul Lee, and Sungsu Lim | heterogeneous graph representation learning, graph neural networks, hyperbolic graph embedding, graph data mining, heterogeneous graph embeding 异构图表示学习，图神经网络，双曲图嵌入，图数据挖掘，异构图嵌入 |
| DM517 | DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model DifFaiRec：条件扩散模型的生成公平推荐器 |
Zhenhao Jiang and Jicong Fan | Recommender System, Group Fairness, Diffusion Model, Counterfactual Module 推荐系统，群体公平性，扩散模型，反事实模块 |
| DM559 | FGLBA: Enabling Highly-Effective and Stealthy Backdoor Attack on Federated Graph Learning FGLBA：实现高效且隐蔽的联邦图学习后门攻击 |
Qing Lu, Miao Hu, Di Wu, Yipeng Zhou, Mohsen Guizani, and Quan Z. Sheng | backdoor attack, federated graph learning 后门攻击，联邦图学习 |
| DM573 | D-Cube : Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification D-Cube：利用扩散模型的超特征进行鲁棒医学分类 |
Minhee Jang, Juheon Son, Thanaporn Viriyasaranon, Junho Kim, and Jang-hwan Choi | Medical Image Classification, Diffusion Models, Feature Selection, Contrastive Learning, Synthetic Data Generation 医学图像分类，扩散模型，特征选择，对比学习，合成数据生成 |
| DM580 | Cascading Multimodal Feature Enhanced Contrast Learning for Music Recommendation 级联多模态特征增强对比学习用于音乐推荐 |
Qimeng Yang, Shijia Wang, Da Guo, Dongjin Yu, Qiang Xiao, Dongjing Wang, and Chuanjiang Luo | music recommendation, representation learning, data bias, contrastive learning, multimodal feature 音乐推荐，表示学习，数据偏差，对比学习，多模态特征 |
| DM583 | Enhancing Entity Alignment on Probabilistic Knowledge Graphs 提升概率知识图谱中的实体对齐 |
Yunfei Li, Lu Chen, Chengfei Liu, Rui Zhou, and Jianxin Li | Probabilistic Knowledge Graph, Entity Alignment, Probabilistic Graph Neural Network, Markov Chain Monte Carlo, Probabilistic Knowledge Graph Embedding 概率知识图谱、实体对齐、概率图神经网络、马尔可夫链蒙特卡洛、概率知识图谱嵌入 |
| DM604 | AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models 异常 LLM：使用大型语言模型的动态图少样本异常边缘检测 |
Shuo Liu, Yao Di, Lanting Fang, Zhetao Li, Wenbin Li, Kaiyu Feng, Xiaowen Ji, and Jingping Bi | Dynamic Graphs, Anomaly Detection, Few-Shot Learning, Large Language Models 动态图，异常检测，小样本学习，大型语言模型 |
| DM605 | SemiFDA: Domain Adaptation in Semi-Supervised Federated Learning 半监督联邦学习中的领域自适应：SemiFDA |
Michele Craighero, Giorgio Rossi, Beatrice Rossi, Diego Carrera, Diego Stucchi, Pasqualina Fragneto, and Giacomo Boracchi | Human Activity Recognition, semi-supervised, domain shift, federated learning, features alignment 人类活动识别，半监督，领域迁移，联邦学习，特征对齐 |
| DM617 | IIFE: Interaction Information Based Automated Feature Engineering IIFE：基于交互信息自动特征工程 |
Tom Overman, Diego Klabjan, and Jean Utke | Automated Feature Engineering, Feature Engineering, Automated Data Science 自动化特征工程，特征工程，自动化数据科学 |
| DM628 | Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment 无监督领域自适应通过自集成和条件嵌入对齐进行动作识别 |
Indrajeet Ghosh, Garvit Chugh, Abu Zaher Md Faridee, and Nirmalya Roy | Unsupervised Domain Adaptation, Conditional Alignment, Wearable-based Action Recognition, Consistency Regularization, Temporal Ensembling 无监督领域自适应，条件对齐，基于可穿戴的动作识别，一致性正则化，时间集成 |
| DM648 | Survival Analysis with Multiple Noisy Labels 生存分析中的多重噪声标签 |
Donna Tjandra and Jenna Wiens | Health Application, Survival Analysis, Time-to-Event Prediction, Noisy Labels, Multiple Labelers 健康应用，生存分析，事件发生时间预测，噪声标签，多个标注者 |
| DM649 | Controllable Visit Trajectory Generation with Spatiotemporal Constraints 可控的具有时空约束的访问轨迹生成 |
Haowen Lin, John Krumm, Cyrus Shahabi, and Li Xiong | Spatial-temporal systems, Controlled generation 空间-时间系统，受控生成 |
| DM663 | A Parameter Update Balancing Algorithm for Multi-task Ranking Models in Recommendation Systems 多任务推荐系统中的参数更新平衡算法 |
Jun Yuan, Guohao Cai, and Zhenhua Dong | Multi-task Optimization, Recommendation System 多任务优化，推荐系统 |
| DM672 | Reducing Unfairness in Distributed Community Detection 降低分布式社区检测中的不公平性 |
Hao Zhang, Malith Jayaweera, Bin Ren, Yanzhi Wang, and Sucheta Soundarajan | big graph data, community detection, big data processing fairness 大型图数据，社区检测，大数据处理公平性 |
| DM681 | Graph Rhythm Network: Beyond Energy Modeling for Deep Graph Neural Networks 图节奏网络：超越能量建模的深度图神经网络 |
Yufei Jin and Xingquan Zhu | Graph rhythm, graph neural network, oversmoothing, graph embedding 图节奏，图神经网络，过平滑，图嵌入 |
| DM708 | An Explainable Recommender System by Integrating Graph Neural Networks and User Reviews 一个通过整合图神经网络和用户评论的可解释推荐系统 |
Sahar Batmani, Parham Moradi, Narges Haidari, and Mahdi Jalili | Recommender System, Explainability, Graph Neural Networks, Temporal Convolution Networks, User Reviews 推荐系统，可解释性，图神经网络，时序卷积网络，用户评价 |
| DM726 | ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction ExoTST：用于时间序列预测的外源感知时间序列转换器 |
Kshitij Tayal, Arvind Renganathan, Xiaowei Jia, Vipin Kumar, and Dan Lu | Exogenous Variables, Modality Fusion 外生变量，模态融合 |
| DM729 | Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization 提升图域外泛化中的分布和标签一致性 |
Song Wang, Xiaodong Yang, Rashidul Islam, Huiyuan Chen, Minghua Xu, Jundong Li, and Yiwei Cai | Graph Neural Networks, Distribution Shifts, Out-of-Distribution (OOD) Generalization 图神经网络，分布偏移，分布外（OOD）泛化 |
| DM741 | CAKD: A Correlation-Aware Knowledge Distillation Framework Based on Decoupling Kullback-Leibler Divergence CAKD：基于解耦 Kullback-Leibler 散度的关联感知知识蒸馏框架 |
Zao Zhang, Huaming Chen, Pei Ning, and Dong Yuan | Knowledge Distillation, Model Compression, Feature Interpretability. 知识蒸馏，模型压缩，特征可解释性。 |
| DM749 | Addressing Delayed Feedback in Conversion Rate Prediction: A Domain Adaptation Approach 解决转化率预测中的延迟反馈：一种领域自适应方法 |
Leisheng Yu, Yanxiao Cai, Lucas Chen, Minxing Zhang, Wei-Yen Day, Li Li, Rui Chen, Soo-Hyun Choi, and Xia Hu | Delayed Feedback, Computational Advertising 延迟反馈，计算广告 |
| DM753 | Hypergraph-Enhanced Contrastively Regularized Transformer for Multi-Behavior E-commerce Product Recommendation 超图增强对比正则化 Transformer 在多行为电子商务产品推荐中的应用 |
Shuiying Liao and P. Y. Mok | Recommendation, Personalization, E-commerce, Preference Modeling, Data Augmentation. 推荐，个性化，电子商务，偏好建模，数据增强。 |
| DM780 | An Efficient Graph Autoencoder with Lightweight Desmoothing Decoder and Long-Range Modeling 高效图自编码器：轻量级去平滑解码器和长距离建模 |
Jinyong Wen, Chunxia Zhang, Shiming Xiang, and Chunhong Pan | self-supervised graph representation learning, graph autoencoder, lightweight smoothness-aware feature reconstructor, global structural dependency catcher 自监督图表示学习，图自动编码器，轻量级平滑度感知特征重构器，全局结构依赖捕捉 |
| DM795 | Handling Non-IID Data in Federated Learning Using Metaheuristic Optimization Techniques 处理联邦学习中非独立同分布数据使用元启发式优化技术 |
Amin Birashk, Sadaf MD Halim, and Latifur Khan | Federated Learning, Non-IID Data, Metaheuristic Optimization, Statistical Heterogeneity, Privacy Preservation 联邦学习，非独立同分布数据，元启发式优化，统计异质性，隐私保护 |
| DM798 | MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model MoRE-LLM：由大型语言模型引导的规则专家混合体 |
Alexander Koebler, Ingo Thon, and Florian Buettner | Large Language Model, Interpretable AI, Explainable AI, Mixture of Experts 大型语言模型，可解释人工智能，可解释 AI，专家混合 |
| DM809 | PC3: Enhancing Concurrency in High-Conflict Transactions with Prior Cascading Control PC3：通过优先级级联控制增强高冲突事务的并发性 |
Zhibin Wang, Jiangtao Cui, Xiyue Gao, Hui Zhang, Guiqi Ren, Yixiao Liu, Hui Li, and Kankan Zhao | Transaction Concurrency Control, Transaction Prediction, Concurrency Optimization 事务并发控制，事务预测，并发优化 |
| DM812 | Rank Supervised Contrastive Learning for Time Series Classification 时间序列分类的排名监督对比学习 |
Qianying Ren, Dongsheng Luo, and Dongjin Song | time series classification, representation learning, contrastive learning 时间序列分类，表示学习，对比学习 |