Yang Yang 杨洋

Associate Professor, Zhejiang University

Email: yangya {at} zju [dot] edu [dot] cn

Office: Room 415, CGB Building, Yuquan Campus

I am associate professor of Computer Science and Technology at Zhejiang University, serving as head of Artificial Intelligence. I received NSFC for Excellent Young Scholar. I am also scientific advisor at FinVolution Group. My research interests include artificial intelligence in networks, deep learning for large-scale dynamic time-series, and computational social science. I obtained my Ph.D. degree from Tsinghua University in 2016, fortunately advised by Jie Tang and Juanzi Li. I have been visiting Cornell University (working with John Hopcroft) in 2012, and University of Leuven (working with Marie-Francine Moens) in 2013. During my Ph.D. career, I also have Yizhou Sun from UCLA as my research advisor. Here is my CV.

I am looking for highly-motivated students to work with me. If interested, please drop me a message by email. For students requesting reference letters from me, please ensure that we have collaborated for a minimum of six months to provide a comprehensive and useful assessment of your capabilities and contributions.

The goal is to establish a universal model for brain signals, enhancing performance in various downstream tasks within the healthcare domain while empowering a quantitative understanding of brain activity in neuroscience.

Starting from a real medical scenario of seizure detection, we automatically identify epileptic waves in intracranial brain signals for medication-resistant patients, expediting the localization of lesions within the brain. Inspired by neuroscience research, we initially model the diffusion patterns of epileptic waves for individual patients (BrainNet, Chen et al., KDD'22). Subsequently, we employ self-supervised learning to capture universal spatiotemporal correlations between signals from different brain regions, facilitating transferability across different patients (MBrain, Cai et al., KDD'23; PPi, Yuan et al., NeurIPS'23).

To establish a foundational model, we initially endeavor to pretrain a foundational model with 500M parameters based on a large volume of intracranial brain signals (Brant, Zhang et al., NeurIPS'23). Subsequently, we integrate EEG into the pretraining corpus, building a foundational model with 1B parameters, thereby generalizing to a broader range of downstream tasks such as sleep staging and emotion recognition (Brant-2, arXiv:2402.10251). Capitalizing on the robust generalization capabilities of Brant-2, we propose a unified alignment framework (Brant-X) to rapidly adapt Brant-2 to downstream tasks involving rare physiological signals (e.g., EOG/ECG/EMG). In our pursuit to construct a universal foundational model, we recognize the necessity for a comprehensive dataset encompassing a wide array of domains. Confronted with the rarity of brain signal data, we delve into a diffusion-based model, for the generation of intracranial brain signals (NeuralDiff). Moreover, we innovate to synthesize endless sequences, thereby circumventing the dependence on actual data (InfoBoost, arXiv:2402.00607).

Papers: (Chen et al., KDD'22), (Cai et al., KDD'23), (Yuan et al., NeurIPS'23), (Zhang et al., NeurIPS'23), (Brant-2, arXiv:2402.10251), (InfoBoost, arXiv:2402.00607).

The goal is to pre-train a general graph foudnation model using a large corpus of graph data. With appropriate fine-tuning, such a graph foundation model can achieve satisfactory performance across various downstream tasks, showcasing its broad application potential and the numerous challenges it entails.

To achieve this goal, we design base graph models with enhanced expressive capabilities (DropMessage, Fang et al., AAAI'23; PathNet, Sun et al., IJCAI'22) and investigate how to select appropriate pre-training corpora (W2PGNN, Cao et al., KDD'23; Xu et al., NeurIPS'23). We also conduct an in-depth study on the crucial role of pre-training strategies in the construction of the graph foundation model and analyze existing graph self-supervised methods from a unified perspective (GraphTCM, Fang et al., ICML'24). When adapting the pre-trained graph foundation model to downstream tasks, we explore the intrinsic factors that determine the model's final performance (G-Tuning, Sun et al., AAAI'24; Bridge-Tune, Huang et al., AAAI'24) and design various effective and parameter-efficient adaptation methods (GPF, Fang et al., NeurIPS'23; Huang et al., KDD'24). In addition, we have released a large-scale dynamic graph financial network pre-training dataset, DGraph (Huang et al., NeurIPS'22), addressing the lack of graph datasets in this field.

Papers: (Fang et al., AAAI'23), (Cao et al., KDD'23), (Xu et al., NeurIPS'23), (Fang et al., ICML'24), (Sun et al., AAAI'24), (Sun et al., IJCAI'22), (Huang et al., AAAI'24), (Fang et al., NeurIPS'23), (Huang et al., NeurIPS'22)

Codes: [DropMessage] [PathNet] [W2PGNN] [GraphTCM] [G-Tuning] [Bridge-Tune] [GPF]

Dataset: [DGraph]

The goal is to enhance the versatility of Large Language Models (LLMs) across specialized domains. Driven by the reality that many real-world applications, such as financial risk management and power grid scheduling, demand a multidisciplinary strategy, we take inspiration from human ingenuity. Humans have a remarkable ability to navigate complex issues by integrating a wide array of expertise through collaborative efforts. With this in mind, we explore the dynamics of LLMs when they collaborate with domain-specific models to tackle challenging real-world problems.

We embark on our journey by examining the synergy between LLMs and Graph Neural Networks (GNNs). GNNs are inherently crafted for processing graph data, a prevalent format in real-world scenarios. We investigate how LLMs can collaborate with GNN to boost its graph reasoning capability (GraphLLM, arXiv:2310.05845). Additionally, we study the possibility of LLMs and GNNs collaborating through an innovative framework that positions GNNs as a unique class of adapter modules (GraphAdapter, Huang et al., WWW'24). Furthermore, we explore how LLMs can collaborate specialized agents (ETR, Chai et al., ACL'24), where a unified generalist framework is built to facilitate seamless integration of multiple expert LLMs. In addition to our theoretical explorations, we have launched key datasets to assess LLMs in specific domains. For graph-related tasks, we introduce a new dataset from social media, merging text and graph data (Huang et al., WWW'24). Additionally, for analyzing LLM-based agents' data analytics capabilities, we publish the InfiAgent-DABench benchmark (InfiAgent, Hu et al., ICML'24).

Papers: [Chai et al., ACL'24] (Huang et al., WWW'24), (Hu et al., ICML'24), (GraphLLM, arXiv:2310.05845).

Codes: [ETR], [GraphLLM], [GraphAdapter]

Benchmark: [InfiAgent]


2024
  • Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang, Xuanwen Huang, Xuwu Wang, Jingjing Xu, Jianbo Yuan, Hongxia Yang, Fei Wu, and Yang Yang. An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing. In Proceedings of the 62th Annual Meeting of the Association of Computational Linguistics (ACL'24). [PDF] [Code]
  • Taoran Fang, Wei Zhou, Yifei Sun, Kaiqiao Han, Lvbin Ma, and Yang Yang. Exploring Correlations of Self-Supervised Tasks for Graphs. In Proceedings of the 41st International Conference on Machine Learning (ICML'24). [PDF] [Code]
  • Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, and Fei Wu. InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks. In Proceedings of the 41st International Conference on Machine Learning (ICML'24). [PDF] [InfiAgent]
  • Haoran Deng, Yang Yang, Jiahe Li, Cheng Chen, Weihao Jiang, and Shiliang Pu. Fast Updating Truncated SVD for Representation Learning with Sparse Matrices. In Proceedings of the 13th International Conference on Learning Representations (ICLR'24), 2024. [PDF] [Code]
  • Linfeng Cao, Haoran Deng, Yang Yang, Chunping Wang, and Lei Chen. Graph-Skeleton: ∼1% Nodes are Sufficient to Represent Billion-Scale Graph. In Proceedings of the 33rd Web Conference (WWW'24), 2024. [PDF] [Long Version] [Code]
  • Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao, Quanjin Tao, Ziwei Chai, and Qi Zhu. Can GNN be Good Adapter for LLMs? In Proceedings of the 33rd Web Conference (WWW'24), 2024. [PDF] [Code]
  • Juren Li, Yang Yang, Youmin Chen, Jianfeng Zhang, Zeyu Lai, and Lujia Pan. DWLR: Domain Adaptation under Label Shift for Wearable Sensor. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024. [PDF] [Code]
  • Youmin Chen*, Xinyu Yan*, Yang Yang, Jianfeng Zhang, Jing Zhang, Lujia Pan, and Juren Li. Disentangling Domain and General Representations for Time Series Classification. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024 (*: equal contribution). [PDF] [Code]
  • Yifei Sun, Qi Zhu, Yang Yang, Chunping Wang, Tianyu Fan, Jiajun Zhu, and Lei Chen. Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'24), 2024. [PDF] [Code]
  • Renhong Huang, Jiarong Xu, Xin Jiang, Chenglu Pan, Zhiming Yang, Chunping Wang, and Yang Yang. Measuring Task Similarity and Its Implication in Fine-Tuning Graph Neural Networks. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'24), 2024. [PDF] [Code]
  • Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, and Yang Yang. Towards Fair Graph Federated Learning via Incentive Mechanisms. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'24), 2024. [PDF] [Code]
  • 2023
  • Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, and Yang Yang. DropMessage: Unifying Random Dropping for Graph Neural Networks. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'23), 2023. [PDF] [Code] (Distinguished Paper Award)
  • Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. Universal Prompt Tuning for Graph Neural Networks. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023. [PDF] [Code]
  • Daoze Zhang*, Zhizhang Yuan*, Yang Yang, Junru Chen, Jingjing Wang, and Yafeng Li. Brant: Foundation Model for Intracranial Neural Signal. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023 (*: equal contribution). [PDF] [Website]
  • Zhizhang Yuan*, Daoze Zhang*, Yang Yang, Junru Chen, and Yafeng Li. PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023 (*: equal contribution). [PDF]
  • Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, and Yang Yang. Better with Less: A Data-Centric Prespective on Pre-Training Graph Neural Networks. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023. [PDF]
  • Haoran Deng, Yang Yang, Jiahe Li, Haoyang Cai, Shiliang Pu, and Weihao Jiang. Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds. In Proceedings of the Twenty-Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023. [PDF] [Code]
  • Donghong Cai*, Junru Chen*, Yang Yang, Teng Liu, and Yafeng Li. MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals. In Proceedings of the Twenty-Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023 (*: equal contribution). [PDF]
  • Yuxuan Cao*, Jiarong Xu*, Carl Yang, Jiaan Wang, Yunchao Mercer Zhang, Chunping Wang, Lei Chen, and Yang Yang. When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective! In Proceedings of the Twenty-Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023 (*: equal contribution). [PDF] [Code]
  • Xuan Yang, Yang Yang, Chenhao Tan, Yinghe Lin, Zhengzhe Fu, Fei Wu, and Yueting Zhuang. Unfolding and Modeling the Recovery Process after COVID Lockdowns. In Nature Scientific Reports, 2023. [PDF] [Online] [Poster]
  • Ziwei Chai, Yang Yang, Jiawang Dan, Sheng Tian, Changhua Meng, Weiqiang Wang, and Yifei Sun. Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'23), 2023. [PDF]
  • Boning Zhang and Yang Yang. MediaHG: Rethinking Eye-catchy Features in Social Media Headline Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP'23), 2023. [PDF]
  • Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, and Jie Tang. CogDL: A Comprehensive Library for Graph Deep Learning. In Proceedings of the Web Conference 2023 (WWW'23), 2023. [PDF]
  • 2022
  • Junru Chen*, Yang Yang*, Tao Yu, Yingying Fan, Xiaolong Mo, and Carl Yang. BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning. In Proceedings of the Twenty-Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'22), 2022 (*: equal contribution). [PDF]
  • Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, Lei Chen and Michalis Vazirgiannis. DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS’22), 2022. [PDF] [DGraph Data]
  • Xuan Yang, Yang Yang, Jintao Su, Yifei Sun, Shen Fan, Zhongyao Wang, Jun Zhan, and Jingmin Chen. Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2022 (accepted). [PDF]
  • Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu, and Yizhou Sun. Unsupervised Adversarially Robust Representation Learning on Graphs. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'22), 2022. [PDF] [Code]
  • Ziwei Chai*, Siqi You*, Yang Yang, Shiliang Pu, Jiarong Xu, Haoyang Cai, and Weihao Jiang. Can Abnormality be Detected by Graph Neural Networks? In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), 2022 (*: equal contribution). [PDF] [Code]
  • Yifei Sun, Haoran Deng, Yang Yang, Chunping Wang, Jiarong Xu, Renhong Huang, Linfeng Cao, Yang Wang, and Lei Chen. Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), 2022. [PDF] [Code]
  • Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Chunping Wang, Jiangang Lu, and Yang Yang. Blindfolded Attackers Still Threatning: Strict Black-Box Adversarial Attacks on Graphs. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'22), 2022. [PDF] [Code]
  • Lei Chen, Guanying Li, Zhongyu Wei, Yang Yang, Baohua Zhou, Qi Zhang, Xuanjing Huang. A Progressive Framework for Role-Aware Rumor Resolution. In Proceedings of the 29th International Conference on Computational Linguistics (COLING'22), 2022, pages 2748–2758. [PDF]
  • 2021
  • Jiarong Xu, Yang Yang, Shiliang Pu, Yao Fu, Jun Feng, Weihao Jiang, Jiangang Lu, and Chunping Wang. NetRL: Task-aware Network Denoising via Deep Reinforcement Learning. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2021. [PDF] [Code]
  • Ziqiang Cheng, Yang Yang, Shuo Jiang, Wenjie Hu, Zhangchi Ying, Ziwei Chai, and Chunping Wang. Time2Graph+: Bridging Time Series and Graph Representation Learning via Multiple Attentions. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2021. [PDF] [Code]
  • Xuanwen Huang, Yang Yang, Ziqiang Cheng, Shen Fan, Zhongyao Wang, Juren Li, Jun Zhang, and Jingmin Chen. How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'21), 2021. [PDF]
  • Ping Shao, Yang Yang, Shengyao Xu, and Chunping Wang. Network Embedding via Motifs. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2021. [PDF] [Code]
  • Wenjie Hu, Yang Yang, Ziqiang Cheng, Carl Yang, and Xiang Ren. Time-Series Event Prediction with Evolutionary State Graph. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM'21), 2021. [PDF] [Code] [Demo]
  • Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS’21), 2021. [PDF] [Graph Robustness Benchmark]
  • 2020
  • Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang, and Guojie Song. Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'20), 2020. [PDF] [Code]
  • Wenjie Hu, Yang Yang, Jianbo Wang, Xuanwen Huang, and Ziqiang Cheng. Understanding Electricity-Theft Behavior via Multi-Source Data. In Proceedings of the 29th World Wide Web Conference (WWW'20), 2020. [PDF] [Slides]
  • Jiarong Xu, Yang Yang, Chunping Wang, Zongtao Liu, Jing Zhang, Lei Chen, and Jiangang Lu. Robust Network Enhancement from Flawed Networks. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2020. [PDF] [Code]
  • Xiaobin Tang, Jing Zhang, Bo Chen, Yang Yang, Hong Chen, and Cuiping Li. BERT-INT: A BERT-based Interaction Model For Knowledge Graph Alignment. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'20), 2020. [PDF]
  • Wenjie Hu, Yang Yang, Liang Wu, Zongtao Liu, Zhanlin Sun, and Bingshen Yao. Capturing Evolution Genes for Time Series Data. Preprint. [arxiv]
  • Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, and Chunping Wang. Learning Fair Representations via an Adversarial Framework. Preprint. [arxiv]
  • 2019
  • Yang Yang, Yuhong Xu, Yizhou Sun, Yuxiao Dong, Fei Wu, and Yueting Zhuang. Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2019. [PDF]
  • Zongtao Liu, Yang Yang, Wei Huang, Zhongyi Tang, Ning Li, and Fei Wu. How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation. In Proceedings of the Twenty-Eighth World Wide Web Conference (WWW'19), 2019. [PDF] [Code] [BIB]
  • Ziqiang Cheng, Yang Yang, Chenhao Tan, Denny Cheng, Alex Cheng, and Yueting Zhuang. What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings. In Proceedings of the Twenty-Eighth World Wide Web Conference (WWW'19, short paper), 2019. [PDF] [BIB] [long version on arxiv]
  • Yang Yang*, Yuhong Xu*, Chunping Wang, Yizhou Sun, Fei Wu, Yueting Zhuang, and Ming Gu. Understanding Default Behavior in Online Lending. In Proceedings of the Twenty-Eighth Conference on Information and Knowledge Management (CIKM'19), 2019 (*: equal contribution). [PDF] [BIB]
  • Rui Feng, Yang Yang, Yizhou Sun, and Chunping Wang. A Unified Network Embedding Algorithm for Multi-type Similarity Measures. In 1st International Workshop on Graph Representation Learning and its Applications (GRLA'19), 2019. [PDF] [BIB]
  • 2018
  • Yang Yang, Zongtao Liu, Chenhao Tan, Fei Wu, Yueting Zhuang, and Yafeng Li. To Stay or to Leave: Churn Prediction for Urban Migrants in the Initial Period. In Proceedings of the Twenty-Seventh World Wide Web Conference (WWW'18), 2018, pages 967-976. [PDF] [Slides] [Data] [BIB]
  • Yang Yang, Chenhao Tan, Zongtao Liu, Fei Wu, and Yueting Zhuang. Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 507-514. [PDF] [BIB]
  • Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. Dynamic Network Embedding by Modeling Triadic Closure Process. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 571-578. [PDF] [Code] [BIB]
  • Rui Feng*, Yang Yang*, Wenjie Hu, Fei Wu, and Yueting Zhuang. Representation Learning for Scale-free Networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 282-289 (*: equal contribution). [PDF] [Code] [BIB]
  • Yupeng Gu, Yizhou Sun, Yanen Li, and Yang Yang. RaRE: Social Rank Regulated Large-scale Network Embedding. In Proceedings of the Twenty-Seventh World Wide Web Conference (WWW'18), 2018, pages 359-368. [PDF] [BIB]
  • Menghan Wang, Xiaolin Zheng, Yang Yang, and Kun Zhang. Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 2516-2523. [PDF] [BIB]
  • Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. Reinforcement Learning for Relation Extraction from Noisy Data. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 5779-5786. [PDF] [BIB]
  • 2017
  • Yang Yang, Jie Tang, and Juanzi Li. Learning to Infer Competitive Relationships in Heterogeneous Networks. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2017. [PDF] [BIB]
  • Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, and Yang Yang. User Modeling on Demographic Attributes in Large-Scale Mobile Social Networks. In ACM Transactions on Information Systems (TOIS), 2017, Volume 35, Issue 4. [PDF]
  • Xinyang Jiang, Siliang Tang, Yang Yang, Zhou Zhao, Fei Wu, and Yueting Zhuang. Detecting Temporal Proposal for Action Localization with Tree-structured Search Policy. In Proceedings of the 25th Conference on ACM Multimedia (ACM Multimedia'17), 2017, pages 1069-1077.
  • Yang Yang and Jie Tang. Computational Models for Social Influence and Diffusion In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 2017. (Tutorial)
  • 2016
  • Yang Yang, Jia Jia, Boya Wu, and Jie Tang. Social Role-Aware Emotion Contagion in Image Social Networks. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), 2016, pages 65-71. [PDF] [Data]
  • Jun Feng, Minlie Huang, Yang Yang, and Xiaoyan Zhu. GAKE: Graph Aware Knowledge Emebdding. In Proceedings of the 27th International Conference on Computational Linguistics (COLING'16), 2016, pages 641-651. [PDF]
  • Boya Wu, Jia Jia, Yang Yang , Peijun Zhao, Jie Tang, and Qi Tian. Inferring Emotional Tags From Social Images With User Demographics. In IEEE Transactions on Multimedia (TMM) , 2016, accepted. [PDF]
  • 2015
  • Yang Yang, Yizhou Sun, Jie Tang, Bo Ma, and Juanzi Li. Entity Matching across Heterogeneous Sources. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), 2015, pages 1395-1404. [PDF] [Data&Code]
  • Yang Yang, Jie Tang, Cane Wing-Ki Leung, Yizhou Sun, Qicong Chen, Juanzi Li, and Qiang Yang. RAIN: Social Role-Aware Information Diffusion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), 2015, page 367-373. [PDF] [Data&Code] [Data] [BIB]
  • Yang Yang and Jie Tang. Beyond Query: Interactive User Intention Understanding. In Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM'15), 2015, pages 519-528. [PDF]
  • Boya Wu, Jia Jia, Yang Yang, Peijun Zhao, and Jie Tang. Understanding the Emotions Behind Social Images: Inferring with User Demographics. In Proceedings of 2015 IEEE International Conference on Multimedia and Expo (ICME'15), 2015. [PDF]
  • 2014 and prior
  • Yang Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, Chunxiao Xing, and Jie Tang. How Do Your Friends on Social Media Disclose Your Emotions? In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), 2014, pages 306-312. [PDF] [Data&Code]
  • Yang Yang, Walter Luyten, Lu Liu, Marie-Francine Moens, Jie Tang, and Juanzi Li. Forecasting Potential Diabetes Complications. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), 2014, pages 313-319. [PDF] [Data&Code]
  • Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, and Nitesh V. Chawla. Inferring User Demographics and Social Strategies in Mobile Social Networks. In Proceedings of the Twentyth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), 2014, pages 15-24. [PDF] [Data&Code]
  • Yang Yang, Jianfei Wang, Yutao Zhang, Wei Chen, Jing Zhang, Honglei Zhuang, Zhilin Yang, Bo Ma, Zhanpeng Fang, Sen Wu, Xiaoxiao Li, Debing Liu, and Jie Tang. SAE: Social Analytic Engine for Dynamic Networks. In Proceedings of the Ninteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'13, demo paper), 2013, pages 1502-1505. [PDF] [Poster]
  • Yang Yang, Jie Tang, Jacklyne Keomany, Yanting Zhao, Ying Ding, Juanzi Li, and Liangwei Wang. Mining Competitive Relationships by Learning across Heterogeneous Networks. In Proceedings of the Twenty-First Conference on Information and Knowledge Management (CIKM'12), 2012, pages 1432-1441. [PDF]
  • Jie Tang, Bo Wang, Yang Yang, Po Hu, Yanting Zhao, Xinyu Yan, Bo Gao, Minlie Huang, Peng Xu, Weichang Li, and Adam K. Usadi. PatentMiner: Topic-driven Patent Analysis and Mining. In Proceedings of the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12), 2012, pages 1366-1374. [PDF] [Slides] [Poster] [System] [Video]
  • Tieyun Qian, Yang Yang, Shuo Wang. Refining Graph Partitioning for Social Network Clustering. In Proceeding of the Eleventh International Conference on Web Information System Engineering (WISE'10), 2010, pages 77-90. [PDF]
  • Journal Editors:
  • Associate Editor of IEEE Transactions on Big Data (TBD)
  • Conference Organization:
  • Sponsor co-chair of WWW 2021
  • Program chair of SMP 2020
  • Tutorial and data mining forum at SMP 2018
  • BigNet Wokshop at CIKM 2016
  • KDD Forum at SMP 2016
  • Conference PC Members:
  • KDD 2022, KDD 2021, KDD 2020, WSDM 2020, WSDM 2019 (awarded as Outstanding PC), KDD 2019, AAAI 2019, KDD 2018, WWW 2017, WSDM 2017, CIKM 2017, ICWSM 2017, WSDM 2016, CIKM 2016, ASONAM 2015.
  • 2023, awarded AAAI Distinguished Paper
  • 2016, awarded Outstanding Doctoral Thesis of Chinese Institute of Electronics
  • 2016, awarded Excellent Graduate of Tsinghua University with Ph.D. degree
  • 2016, awarded Excellent Graduate of Beijing City with Ph.D. degree
  • 2014-2015, awarded National Scholarships
  • 2013, awarded Alumnus Scholarship of Tsinghua University
  • 2008-2009, ACM International Collegiate Programming Contest, Asia Regional, 5 gold medals
  • When not doing research, I enjoy playing Magic The Gathering (a world-wide trading card game). I won a Pro Tour Qualifier Tournament in 2014.
  • Ph.D. students
  • Xuanwen Huang (5th year)
  • Yifei Sun (5th year)
  • Junru Chen (4th year)
  • Zhengzhe Fu (4th year)
  • Fanzhe Fu (4th year)
  • Shihao Tu (4th year)
  • Taoran Fang (3rd year)
  • Ziwei Chai (3rd year)
  • Juren Li (3rd year)
  • Chenglu Pan (3rd year, co-advising)
  • Dezheng Bao (1st year)
  • Lekui Zhou (graduated in 2020, work @ Huawei)
  • Jiarong Xu (graudated in 2021, assistant professor @ Fudan University)
  • Master students
  • Zhendong Fu (3rd year)
  • Zhisheng Zhang (3rd year)
  • Yiqin Zhang (3rd year)
  • Huijuan Wang (3rd year)
  • Quanjin Tao (3rd year)
  • Hanyang Yuan (3rd year)
  • Renhong Huang (2nd year)
  • Yuxuan Cao (2nd year)
  • Haoran Deng (2nd year)
  • Xuhang Zhu (2nd year)
  • Youmin Chen (2nd year)
  • Boning Zhang (2nd year)
  • Yupeng Zhang (2nd year)
  • Zhizhang Yuan (1st year)
  • Daoze Zhang (1st year)
  • Hanchen Su (1st year)
  • Zhijian Bao (1st year)
  • Yize Zhu (1st year)
  • Yuhong Xu (graduated in 2019, work @ NetEase)
  • Wenjie Hu (graduated in 2020, work @ Alibaba)
  • Zongtao Liu (graduated in 2020, work @ Alibaba)
  • Wei Huang (graduated in 2020, work @ ChinaTelcom)
  • Chao Zhou (graduated in 2020, work @ NetEase)
  • Ziqiang Cheng (graduated in 2021, work @ ByteDance)
  • Ping Shao (graduated in 2021, work @ Alibaba)
  • Siqi You (graduated in 2021, work @ Meituan)
  • Tao Yu (graduated in 2021, work @ Hikvision)
  • Jintao Su (graduated in 2022, work @ Tencent)
  • Xuan Yang (graduated in 2023, PhD @ Duke University)
  • Zhexuan Lu (graduated in 2023, work @ Uranus Research)
  • Teng Ke (graduated in 2023, work @ Alibaba)
  • Donghong Cai (graduated in 2023, work @ Alibaba)
  • Jiali Xie (graduated in 2023, work @ Lingxi Games)
  • Wei Dong (graduated in 2023, work @ Nanhu Academy)
  • Yihao Shang (graduated in 2023, work @ Alibaba)
  • Undergraduate students
    I list ones working with me at least 6 months. Please let me know if I miss anyone.
  • Jiahe Li
  • Xiao Feng
  • Jiayu Liu
  • Kaiqiao Han
  • Shuning Shang
  • Xiaokang Shen
  • Tianjie Zhang
  • Xinyi Zheng
  • Wei Zhou
  • Rui Feng (graduated in 2019, PhD @ Georgia Tech)
  • Tianqing Fang (graduated in 2019, PhD @ HKUST)
  • Zhanlin Sun (graduated in 2019, master @ CMU)
  • Jiani Yang (graduated in 2021, PhD @ ZJU)
  • Yingying Fan (graduated in 2021, PhD @ ETH)
  • Yunchao Zhang (graduated in 2023, PhD @ CUHK)
  • Zhouxiang Fang (graduated in 2023, master @ Johns Hopkins University)
  • Ruining He (graduated in 2023, master @ University of Chicago)
  • Zeyu Lai (graduated in 2023, PhD @ ZJU)
  • Teng Liu (graduated in 2023, master @ ETH)