{"id":6133,"date":"2025-01-23T08:42:46","date_gmt":"2025-01-23T08:42:46","guid":{"rendered":"https:\/\/www.wsdm-conference.org\/2025\/?page_id=6133"},"modified":"2025-03-12T10:09:40","modified_gmt":"2025-03-12T10:09:40","slug":"paper-presentation-schedule","status":"publish","type":"page","link":"https:\/\/www.wsdm-conference.org\/2025\/paper-presentation-schedule\/","title":{"rendered":"Paper Presentation Schedule"},"content":{"rendered":"
\n \n
\n \n \n
\n \n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 1: Graph Neural Networks and Inferences (11.03, 10:30 \u2013 12:15)<\/span>
<\/span>Session Chair: Marc Najork<\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 1 (11.03, 10:30 \u2013 11:30)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders:<\/strong> <\/strong>Jongwon Park, Heesoo Jung, Hogun Park<\/em><\/p>\n

A Sublinear Algorithm for Approximate Shortest Paths in Large Networks:<\/strong> <\/strong>Sabyasachi Basu, Nadia K\u00f6shima, Talya Eden, Omri Ben Eliezer, C. Seshadhri<\/em><\/p>\n

Polaris:<\/em> Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity: <\/strong>Giulia Preti, Matteo Riondato, Aristides Gionis, Gianmarco De Francisci Morales<\/em><\/p>\n

DiffGraph: Heterogeneous Graph Diffusion Model: <\/em><\/strong>Zongwei Li, Lianghao Xia, Hua Hua, Shijie Zhang, Shuangyang Wang, Chao Huang<\/em>
<\/em><\/strong><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 1 (11.03, 11:30 \u2013 12:15)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation:<\/strong> Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-kwon Kim<\/em><\/p>\n

2- Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding:<\/strong> Zhe Wang, Sheng Zhou, Jiawei Chen, Zhen Zhang, Binbin Hu, Yan Feng, Chun Chen, Can Wang<\/em><\/p>\n

3- S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain:<\/strong> Rui Xia, Yanhua Cheng, Yongxiang Tang, Xiaocheng Liu, Xialong Liu, Lisong Wang, Peng Jiang<\/em><\/p>\n

4- Maintaining k<\/em>-MinHash Signatures over Fully-Dynamic Data Streams with Recovery: <\/strong>Andrea Clementi, Luciano Gual\u00e0, Luca Pep\u00e8 Sciarria, Alessandro Straziota<\/em><\/p>\n

5- Hyperdimensional Representation Learning for Node Classification and Link Prediction: <\/strong>Abhishek Dalvi, Vasant Honavar<\/em><\/p>\n

6- Large Language Model driven Policy Exploration for Recommender Systems:<\/strong> <\/span>Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 2: Recommendation Systems (11.03, 13:45 – 15:30)<\/span>
<\/span>Session Chair: Zhankui He
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 2 (11.03, 13:45 \u2013 14:45)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

A Contrastive Framework with User, Item and Review Alignment for Recommendation:<\/strong> <\/strong>Hoang V. Dong, Yuan Fang, Hady W. Lauw<\/em><\/p>\n

Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation:<\/strong> <\/strong>Mingrui Liu, Sixiao Zhang, Cheng Long<\/em><\/p>\n

MixRec: Heterogeneous Graph Collaborative Filtering:<\/strong> <\/strong>Lianghao Xia, Meiyan Xie, Yong Xu, Chao Huang<\/em><\/p>\n

Review-Based Hyperbolic Cross-Domain Recommendation: <\/strong>Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim<\/em><\/p>\n

VARIUM: Variational Autoencoder for Multi-Interest Representation with Inter-User Memory: <\/em><\/strong>Nhu-Thuat Tran, Hady W. Lauw<\/em>
<\/em><\/strong><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 2 (11.03, 14:45 \u2013 15:30)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- The Initial Screening Order Problem:<\/strong> Jose M. Alvarez, Antonio Mastropietro, Salvatore Ruggieri<\/em><\/p>\n

2- Cross-Domain Pre-training with Language Models for Transferable Time Series Representations:<\/strong> Mingyue Cheng, Xiaoyu Tao, Qi Liu, Hao Zhang, Yiheng Chen, Defu Lian<\/em><\/p>\n

3- Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts:<\/strong> Soojin Yoon, Sungho Ko, TongYoung Kim, SeongKu Kang, Jinyoung Yeo, Dongha Lee<\/em><\/p>\n

4- Q-DISCO: Query-Centric Densest Subgraphs in Networks with Opinion Information: <\/strong>Tianyi Chen, Atsushi Miyauchi, Charalampos E. Tsourakakis<\/em><\/p>\n

5- Self-supervised Time-aware Heterogeneous Hypergraph Learning for Dynamic Graph-level Classification:<\/strong> Malik Khizar Hayat, Shan Xue, Jia Wu, Bilal Khan, Jian Yang<\/em><\/p>\n

6- Combating Heterogeneous Model Biases in Recommendations via Boosting:<\/strong> Jinhao Pan, James Caverlee, Ziwei Zhu<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 3: Large Language Models (11.03, 16:00 – 17:45)<\/span>
<\/span>Session Chair: Jaap Kamps
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 3 (11.03, 16:00 \u2013 17:00)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework:<\/strong> Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, Qing He<\/em><\/p>\n

MoKGNN: Boosting Graph Neural Networks via Mixture of Generic and Task-Specific Language Models:<\/strong> Hao Yan, Chaozhuo Li, Jun Yin, Weihao Han, Hao Sun, Senzhang Wang, Jian Zhang, Jianxin Wang<\/em><\/p>\n

Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation:<\/strong> Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla<\/em><\/p>\n

Large Language Model Simulator for Cold-Start Recommendation:<\/strong> Feiran Huang, Yuanchen Bei, Zhenghang Yang, Junyi Jiang, Hao Chen, Qijie Shen, Senzhang Wang, Fakhri Karray, Philip S Yu<\/em><\/p>\n

Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation:<\/strong> Chenyuan Wu, Ninglu Shao, Zheng Liu, Shitao Xiao, Chaozhuo Li, Chen Zhang, Senzhang Wang, Defu Lian<\/em>
<\/em><\/strong><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 3 (11.03, 16:00 – 17:45)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Robustness Verification of Deep Graph Neural Networks Tightened by Linear Approximation:<\/strong> Xingyu Zeng, Han Li, Qi Qi, Jingyu Wang, Haodong Deng, Haifeng Sun, Zirui Zhuang, Jianxin Liao<\/em><\/p>\n

2- Dynamic Interaction-Driven Intent Evolver with Semantic Probability Distributions:<\/strong> Zelin Li, Cheng Zhang, Dawei Song<\/em><\/p>\n

3- DDualSE: Decoupled Dual-head Squeeze and Excitation Attention for Sequential Recommendation:<\/strong> Nijia Mo, Jianxiang Zang, Zhan Wang, Hui Liu<\/em><\/p>\n

4- Do Stubborn Users Always Cause More Polarization and Disagreement? A Mathematical Study:<\/strong> Mohammad Shirzadi, Ahad N. Zehmakan<\/em><\/p>\n

5- HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning:<\/strong> Qiuyu Zhu, Liang Zhang, Qianxiong Xu, Kaijun Liu, Cheng Long, Xiaoyang Wang<\/em><\/p>\n

6- Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation:<\/strong> Siqing Zhang, Yuchen Ding, Wei Tang, Wei Sun, Yong Liao, Peng Yuan Zhou<\/em><\/p>\n

7- Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems:<\/strong> Hongliu Cao<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 4: Sequential and Temporal Data Modeling (12.03, 10:30 – 12:15)<\/span>
<\/span>Session Chair: Omar Alonso
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 4 (12.03, 10:30 \u2013 11:30)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

Sequential diversification with provable guarantees:<\/strong> Honglian Wang, Sijing Tu, Aristides Gionis<\/em><\/p>\n

Temporal Linear Item-Item Model for Sequential Recommendation:<\/strong> Seongmin Park, Mincheol Yoon, Minjin Choi, Jongwuk Lee<\/em><\/p>\n

Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation:<\/strong> Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu<\/em><\/p>\n

Neo-TKGC: Enhancing Temporal Knowledge Graph Completion with Integrated Node Weights and Future Information:<\/strong> Zihan Qiu, Xiaoling Zhou, Chunyan An, Qiang Yang, Zhixu Li<\/em><\/p>\n

\ufeff<\/span>Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data:<\/strong> <\/strong>Binbin Hu, Zhicheng An, Zhengwei Wu, Ke Tu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Yufei Feng, Jiawei Chen<\/em>
<\/em><\/strong><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 4 (12.03, 11:30 – 12:15)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- BAKER: Bayesian Kernel Uncertainty in Domain-Specific Document Modelling:<\/strong> Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel<\/em><\/p>\n

2- Edge Classification on Graphs: New Directions in Topological Imbalance:<\/strong> Xueqi Cheng, Yu Wang, Yunchao Liu, Yuying Zhao, Charu C. Aggarwal, Tyler Derr<\/em><\/p>\n

3- Hawkes Point Process-enhanced Dynamic Graph Neural Network:<\/strong> Zhiqiang Wang, Baijing hu, Kaixuan Yao, Jiye Liang<\/em><\/p>\n

4- Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks:<\/strong> Junliang Luo, Xue Liu<\/em><\/p>\n

5- SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation:<\/strong> Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park<\/em><\/p>\n

6- Towards Personalized Federated Multi-Scenario Multi-Task Recommendation:<\/strong> Yue Ding, Yanbiao Ji, Xun Cai, Xin Xin, Yuxiang Lu, Suizhi Huang, Chang Liu, Xiaofeng Gao, Tsuyoshi Murata, Hongtao Lu<\/em><\/p>\n

7- ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning:<\/strong> Zhongnian Li, Meng Wei, Peng Ying, Xinzheng Xu<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 5: Graph Learning and Adaptation (12.03, 13:45 – 15:30)<\/span>
<\/span>Session Chair: Karin Becker
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 5 (12.03, 13:45 \u2013 14:45)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning:<\/strong> Pengyang Zhou, Chaochao Chen, Weiming Liu, Xinting Liao, Fengyuan Yu, Zhihui Fu, Xingyu Lou, Wu Wen, Xiaolin Zheng, Jun Wang<\/em><\/p>\n

Graph Size-imbalanced Learning with Energy-guided Structural Smoothing:<\/strong> Jiawen Qin, Pengfeng Huang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Jianxin Li<\/em><\/p>\n

RSM: Reinforced Subgraph Matching Framework with Fine-grained Operation based Search Plan:<\/strong> Ziming Li, Yuequn Dou, Youhuan Li, Xinhuan Chen, Chuxu Zhang<\/em><\/p>\n

Incomplete Multi-view Clustering via Local Reasoning and Correlation Analysis:<\/strong> Xiaocui Li, Guoliang Li, Xinyu Zhang, Yangtao Wang, Qingyu Shi, Wei Liang<\/em><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 5 (12.03, 14:45 – 15:30)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection:<\/strong> Jiazhen Chen, Mingbin Feng, Tony S. Wirjanto<\/em><\/p>\n

2- Context Embeddings for Efficient Answer Generation in Retrieval-Augmented Generation:<\/strong> David Rau, Shuai Wang, Herv\u00e9 D\u00e9jean, St\u00e9phane Clinchant, Jaap Kamps<\/em><\/p>\n

3- An aspect performance-aware hypergraph neural network for review-based recommendation:<\/strong> Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang<\/em><\/p>\n

4- Balancing Revenue and Privacy with Signaling Schemes in Online Ad Auctions:<\/strong> Hongtao Liu, Luxi Chen, Yiming Ding, Changcheng Li, Han Li, Peng Jiang, Weiran Shen<\/em><\/p>\n

5- DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior Recommendation:<\/strong> Shuai Zhang, Hua Chu, Jianan Li, Yangtao Zhou, Shirong Wang, Qiaofei Sun<\/em><\/p>\n

6- Progressive Tasks Guided Multi-Source Network for Customer Lifetime Value Prediction in Online Advertising:<\/strong> Zheng Pan, Xingyu Lou, Xiao Jin, Chiye Ou, Feng Liu, Tieyong Zeng, Chengwei He, Xiang Liu, Lilong Wei, Jun Wang<\/em><\/p>\n

7- Mining Topics towards ChatGPT Using a Disentangled Contextualized-neural Topic Model:<\/strong> Rui Wang, Xing Liu, Yanan Wang, Shuyu Chang, Yuanzhi Yao, Haiping Huang<\/em><\/p>\n

8- LightGNN: Simple Graph Neural Network for Recommendation:<\/strong>\ufeff<\/em> Guoxuan Chen, Lianghao Xia, Chao Huang<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 6: Fake News and Anomaly Detection (12.03, 16:00 – 17:45)
<\/span>Session Chair: Meeyoung Cha
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 6 (12.03, 16:00 \u2013 17:00)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

Sarcasm detection on twitter: A behavioral modeling approach:<\/strong> Ashwin Rajadesingan, Reza Zafarani and Huan Liu (Test of time award)<\/strong><\/p>\n

Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness:<\/strong> Junghoon Kim, Junmo Lee, Yeonjun In, Kanghoon Yoon, Chanyoung Park<\/em><\/p>\n

D<\/strong>\n 2<\/strong><\/sup>: Customizing Two-Stage Graph Neural Networks for Early Rumor Detection through Cascade Diffusion Prediction:<\/strong> Haowei Xu, Xianghua Li, Chao Gao, Zhen Wang<\/em><\/p>\n

Adjacent Neighborhood Transformer-based Diffusion Model for Anomaly Detection under Incomplete Industrial Data Sources:<\/strong> Chengqing Li, Lulu Wang<\/em><\/p>\n

GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection:<\/strong> Lingzhi Shen, Yunfei Long, Xiaohao Cai, Imran Razzak, Guanming Chen, Kang Liu, Shoaib Jameel<\/em><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 6 (12.03, 17:00 – 17:45)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Enhancing Code Search Intent with Programming Context Exploration:<\/strong> Yanmin Dong, Zhenya Huang, zheng zhang, GuanHao Zhao, Likang Wu, Hongke Zhao, Binbin Jin, Qi Liu<\/em><\/p>\n

2- AMLCDR: An Adaptive Meta-Learning Model for Cross-Domain Recommendation by Aligning Preference Distributions:<\/strong> Fanqi Meng, Zhiyuan Zhang<\/em><\/p>\n

3- HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection:<\/strong> Anran Zhang, Xingfen Wang, Yuhan Zhao<\/em><\/p>\n

4- IMPO: Interpretable Memory-based Prototypical Pooling:<\/strong> Alessio Ragno, Roberto Capobianco<\/em><\/p>\n

5- Personalised Outfit Recommendation via History-aware Transformers:<\/strong> Myong Chol Jung, Julien Monteil, Philip Schulz, Volodymyr Vaskovych<\/em><\/p>\n

6- DTPN: A Diffusion-based Traffic Purification Network for Tor Website Fingerprinting:<\/strong> Chenchen Yang, Xi Xiao, guangwu hu, Zhen Ling, Hao Li, Bin Zhang<\/em><\/p>\n

7- Density-aware and Cluster-based Federated Anomaly Detection on Data Streams:<\/strong> Bin Li, Li Cheng, Zheng Qin, Yunlong Wu<\/em><\/p>\n

\ufeff8- <\/span>Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task Learning: <\/strong>\n Shijie Zhu, Hui Zhao, Tianshu Wu, Pengjie Wang, Hongbo Deng, Jian Xu, Bo Zheng<\/em>\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 7: Bias in Recommendations (13.03, 10:30 – 12:15)
<\/span>Session Chair: David Carmel
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 7 (13.03, 10:30 \u2013 11:30)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective:<\/strong> Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou, Binbin Hu, Yan Feng, Chun Chen, Can Wang<\/em><\/p>\n

Exploration and Exploitation of Hard Negative Samples for Cross-Domain Sequential Recommendation:<\/strong> Yidan Wang, Xuri Ge, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Zhumin Chen, Jun Ma, Xin Xin<\/em><\/p>\n

Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs:<\/strong> Yilong Wang, Tianxiang Zhao, Zongyu Wu, Suhang Wang<\/em><\/p>\n

Your causal self-attentive recommender hosts a lonely neighborhood:<\/strong> Yueqi Wang, Zhankui He, Zhenrui Yue, Julian McAuley, Dong Wang<\/em><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 7 (13.03, 11:30 – 12:15)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Training MLPs on Graphs without Supervision:<\/strong> Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye<\/em><\/p>\n

2- Explainable CTR prediction via LLM reasoning:<\/strong> Xiaohan Yu, Li Zhang, Chong Chen<\/em><\/p>\n

3- Adaptive Graph Enhancement for Imbalanced Multi-relation Graph Learning:<\/strong> Yiyue Qian, Tianyi Ma, Chuxu Zhang, Yanfang Ye<\/em><\/p>\n

4- DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models:<\/strong> Wuchao Li, Rui Huang, Haijun Zhao, Chi Liu, Kai Zheng, Qi Liu, Na Mou, Guorui Zhou, Defu Lian, Yang Song, Wentian Bao, Enyun Yu, Wenwu Ou<\/em><\/p>\n

5- An Edge-Based Decomposition Framework for Temporal Networks:<\/strong> Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano<\/em><\/p>\n

6- Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models:<\/strong> Kexin Zhang, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Kaize Ding, Xiuqiang He, Xue Liu<\/em><\/p>\n

7- Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction:<\/strong> Qinyuan Wu, Mohammad Aflah Khan, Soumi Das, Vedant Nanda, Bishwamittra Ghosh, Camila Kolling, Till Speicher, Laurent Bindschaedler, Krishna Gummadi, Evimaria Terzi<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 8: Multimodal Data and Time Series Analysis (13.03, 13:45 – 15:30)
<\/span>Session Chair: Gerard de Melo
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 8 (13.03, 13:45 \u2013 14:45)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

MedTransTab: Advancing Medical Cross-Table Tabular Data Generation:<\/strong> Yuyan Chen, Qingpei Guo, Shuangjie You, Zhixu Li<\/em><\/p>\n

Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation:<\/strong> Rongqing Kenneth Ong, Andy W. H. Khong<\/em><\/p>\n

InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling:<\/strong> <\/span>Mingyue Cheng, Yiheng Chen, Qi Liu, Zhiding Liu, Yucong Luo, Enhong Chen<\/em><\/p>\n

Inductive Graph Few-shot Class Incremental Learning:<\/strong> Yayong Li, Peyman Moghadam, Can Peng, Nan Ye, Piotr Koniusz<\/em><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 8 (13.03, 14:45 – 15:30)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Improving FIM Code Completions via Context & Curriculum Based Learning:<\/strong> Hitesh Sagtani, Rishabh Mehrotra, Beyang Liu<\/em><\/p>\n

2- Sequentially Diversified and Accurate Recommendations in Chronological Order for a Series of Users:<\/strong> Jongjin Kim, U Kang<\/em><\/p>\n

3- Untapping the Power of Indirect Relationships in Entity Summarization:<\/strong> Atefeh Moradan, Mohammad Sorkhpar, Atsushi Miyauchi, Davide Mottin, Ira Assent<\/em><\/p>\n

4- Heterophilic Graph Neural Networks Optimization with Causal Message-passing:<\/strong> Botao Wang, Jia Li, Heng Chang, Keli Zhang, Fugee Tsung<\/em><\/p>\n

5- HaGAR: Hardness-aware Generative Adversarial Recommender:<\/strong> Yuan-Heng Lee, Josh Jia-Ching Ying, Vincent S. Tseng<\/em><\/p>\n

6- UIPN: User Intent Profiling Network for Multi Behavior Modeling in CTR Prediction:<\/strong> Xu Yang, Guangyuan Yu, Jun He<\/em><\/p>\n

7- DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems:<\/strong> Jiaju Chen, Chongming Gao, Shuai Yuan, Shuchang Liu, Qingpeng Cai, Peng Jiang<\/em><\/p>\n

8- Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation:<\/strong> Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian McAuley<\/em><\/p>\n

7- Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration:<\/strong> Hongji Li, Hanwen Du, Youhua li, Junchen Fu, Chunxiao Li, Ziyi Zhuang, Jiakang Li, Yongxin Ni<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n \n
\n \n
\n
\n \n \n
\n \n
\n <\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n
\n \n
\n
\n \n \n
\n \n
\n \n
\n \n \n

Plenary Session 9: Emerging Topics in Data Mining (13.03, 16:00 – 17:45)
<\/span>Session Chair: Emine Yilmaz
<\/span><\/h2>\n <\/div>\n <\/div>\n
\n \n
\n \n \n

Plenary Session 9 (16:00 \u2013 17:00)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

Improving CTR Prediction with Graph-Enhanced Interest Networks for Sparse Behavior Sequences:<\/strong> Xuanzhou Liu, Zhibo Xiao, Luwei Yang, Hansheng Xue, Jianxing Ma, Yujiu Yang<\/em><\/p>\n

Predicting Eviction Status Using Airbnb Data in the Absence of Ground-Truth Eviction Records:<\/strong> Maryam Tabar, Anusha Abdulla, J. Andrew Petersen, Dongwon Lee<\/em><\/p>\n

Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation:<\/strong> SeongKu Kang, Bowen Jin, Wonbin Kweon, Yu Zhang, Dongha Lee, Jiawei Han, Hwanjo Yu<\/em><\/p>\n

Demystify Epidemic Containment in Directed Networks: Theory and Algorithms:<\/strong> Yinhan He, Chen Chen, Song Wang, Guanghui Min, Jundong Li<\/em><\/p>\n <\/div>\n <\/div>\n

\n \n
\n \n \n

Poster Session 9 (13.03, 17:00 – 17:45)<\/h3>\n <\/div>\n <\/div>\n
\n \n \n
\n

1- Adaptive Loss-based Curricula for Neural Team Recommendation:<\/strong> Reza Barzegar, Marco Kurepa, Hossein Fani<\/em><\/p>\n

2- How Does Memorization Impact LLMs’ Social Reasoning? An Assessment using Seen and Unseen Queries:<\/strong> Maryam Amirizaniani, Maryna Sivachenko, Adrian Lavergne, Chirag Shah, Afra Mashhadi<\/em><\/p>\n

3- RetriEVAL: Evaluating Text Generation with Contextualized Lexical Match:<\/strong> Zhen Li, Xinchi Li, Chongyang Tao, Jiazhan Feng, Tao Shen, Can Xu, Hao Wang, Dongyan Zhao, Shuai Ma<\/em><\/p>\n

4- MCRanker: Generating Diverse Criteria On-the-Fly to Improve Pointwise LLM Rankers:<\/strong> Fang Guo, Wenyu Li, Honglei Zhuang, Yun Luo, Yafu Li, Le Yan, Qi Zhu, Yue Zhang<\/em><\/p>\n

5- Quam: Adaptive Retrieval through Query Affinity Modelling:<\/strong> Mandeep Rathee, Sean MacAvaney, Avishek Anand<\/em><\/p>\n

6- ProCC: Programmatic Reinforcement Learning for Efficient and Transparent TCP Congestion Control:<\/strong> Yin Gu, Kai Zhang, Qi Liu, Runlong Yu, Xin Lin, Xinjie Sun<\/em><\/p>\n

7- UniGLM: Training One Unified Language Model for Text-Attributed Graphs Embedding:<\/strong> Yi Fang, Dongzhe Fan, Sirui Ding, Ninghao Liu, Qiaoyu Tan<\/em><\/p>\n

8- HTEA: Heterogeneity-aware Embedding Learning for Temporal Entity Alignment:<\/strong> Jiayun Li, Wen Hua, Fengmei Jin, Xue Li<\/em><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

Plenary Session 1: Graph Neural Networks and Inferences (11.03, 10:30 \u2013 12:15)Session Chair: Marc Najork Plenary Session 1 (11.03, 10:30 \u2013 11:30) CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders: Jongwon Park, Heesoo Jung, Hogun Park A Sublinear Algorithm for Approximate Shortest Paths in Large Networks: Sabyasachi Basu, Nadia K\u00f6shima, Talya Eden, Omri Ben […]<\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"rank_math_lock_modified_date":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-6133","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/pages\/6133","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/comments?post=6133"}],"version-history":[{"count":14,"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/pages\/6133\/revisions"}],"predecessor-version":[{"id":6667,"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/pages\/6133\/revisions\/6667"}],"wp:attachment":[{"href":"https:\/\/www.wsdm-conference.org\/2025\/wp-json\/wp\/v2\/media?parent=6133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}