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 dean of Artificial Intelligence. 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. During my Ph.D. career, I have been visiting Cornell University (working with John Hopcroft) in 2012, and University of Leuven (working with Marie-Francine Moens) in 2013. I also have Yizhou Sun from UCLA as my external 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.

Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem.

Our recent work proposes to model time series from the perspective of graphs. More specifically, we aim to capture the intrinsic factors and their transitions behind the time series, and describe how these factors affect the time series evolution. To achieve this, we respectively propose the shapelet based method (Time2Graph, Cheng et al., AAAI'20; Time2Graph+, Cheng et al., TKDE'21) and a dynamic graph neural network based model (EvoNet, Hu et al., WSDM'21). Our proposed methods not only achieves clear improvements comparing with state-of-the-art baselines in many tasks, but also provide valuable insights towards explaining the results of prediction results.

Our work has been applied in real-world scenarios, such as network traffic anomaly monitor, as a common service of Alicloud, and electricity-theft behavior detection (Hu et al., WWW'20), collaborated with Alibaba and State Grid Corporation of China.

Related papers: (Cheng et al., TKDE'21), (Cheng et al., AAAI'20), (Hu et al., WSDM'21), (Hu et al., WWW'20)

Related codes: [Time2Graph], [Time2Graph+], [EvoNet]

Network data in real-world tends to be error-prone due to incomplete sampling or imperfect measurements. This in turn results in inaccurate results when performing network analysis or modeling, such as node classification and link prediction, on these flawed networks.

Our research aims to reconstruct a reliable network from a flawed one, a process referred to network enhancement. More specifically, network enhancement aims to detect the noisy links that are observed in the network but should not exist in the real world, as well as to complement the missing links that do indeed exist in the real world yet remain unobserved.

From one perspective, we turn the network enhancement problem into edge sequences generation, and employ a deep reinforcement learning framework to solve it, which takes advantage of downstream task to guide the network denoising process (NetRL, Xu et al., TKDE'21). From another perspective, we construct a self-supervised learning framework that identifies missing links and nosiy links simultaneously by leveraging the mutual influence of them (E-Net, Xu et al., TKDE'20)

Moreover, we study the model robustness against adversarial attacks. Our work shows that even without any information about the target model, one can still perform effective attacks (Xu et al., arxiv). To handle such perturbations, we further propose an unsupervised defense technique to robustify pre-trained deep graph models (Xu et al., arxiv).

Related papers: (Xu et al., TKDE'20), (Xu et al., TKDE'21).

Related codes: [NetRL], [E-Net]

The goal is to understand and detect abnormal vertexes (e.g., users with anomalous behaviors) in large-scale social and information networks. Our work has been widely applied in many scenarios.

In telecommunications field, we propose to spot telemarketing frauds, with an emphasis on unveiling the "precise fraud" phenomenon and the strategies that are used by fraudsters to precisely select targets (Yang et al., TKDE'19). Our study is conducted on a one-month complete dataset of telecommunication metadata in Shanghai with 54 million anonymous users and 698 million call logs.

In financial field, we unearth the correlation between users' anomalous behaviors and their communication network structure in an online lending platform. Moreover, we propose a novel problem: how to identify muti-type fraudsters (Yang et al., CIKM'19)? Our proposed framework can uniformly identify two types of frauds: default borrowers, who will default on a loan to the platform, and cheating agents, who recruit and teach borrowers to cheat by providing false information and faking application materials.

Related papers: (Yang et al., TKDE'19), (Yang et al., CIKM'19)

Graph embedding, also known as network representation learning, aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the graph is preserved.

Our research mainly focuses on learning representations for social networks. Comparing with other networks, social networks have unique properties. For example, social networks are dynamic and evolving over time, caused by user interactions and unstable user relations. We study how to preserve both structural information and temporal information of a given social network, by modeling triadic closure process (Zhou et al., AAAI'18). In particular, the general idea is to impose triad, which is a group of three vertices and is one of the basic units of networks. We model how a closed triad, which consists of three vertices connected with each other, develops from an open triad that has two of three vertices not connected with each other. This triadic closure process is a fundamental mechanism in the formation and evolution of networks, thereby makes our model being able to capture the network dynamics and to learn representation vectors for each vertex at different time steps.

Besides, social networks are scale-free: vertex degrees of a social network follow a heavy-tailed distribution. Is it possible to reconstruct a scale-free network according to the learned vertex embedding? We first theoretically analyze the difficulty of embedding and reconstructing a scale-free network in the Euclidean space, by converting our problem to the sphere packing problem. Then, we propose the "degree penalty" principle for designing scale-free property preserving network embedding algorithm: punishing the proximity between high-degree vertexes. We introduce two implementations of our principle by utilizing the spectral techniques and a skip-gram model respectively (Feng et al., AAAI'18).

Related papers: (Zhou et al., AAAI'18), (Feng et al., AAAI'18), (Gu et al., WWW'18)

Related codes and data: [DynamicTriad] [DP-Spectral]

An unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses both significant challenges for policymakers and important questions for researchers.

To understand the process of migrant integration and help more migrants to realize their urban dreams, we have some exciting ongoing work. We employ a user telecommunication metadata in Shanghai and study systematic differences between locals and migrants in their mobile communication networks and geographical locations (Yang et al., AAAI'18). By distinguishing new migrants (who recently moved to a new city) from settled migrants (who have been in a new city for a while), we demonstrate the integration process of new migrants. The left figure shows geographical distributions of locals, settled migrants and new migrants in Shanghai. Moreover, we investigate migrants’ behavior in their first weeks and in particular, how their behavior relates to early departure (Yang et al., WWW'18), by further employing a novel housing price dataset.

We hope that our study can encourage more researchers in our community to examine the problem of migrant integration from different perspectives and eventually lead to methodologies and applications that benefit policymaking and millions of migrants.

Related papers: (Yang et al., WWW'18), (Yang et al., AAAI'18)

Media coverage: (NewScientist), (浙江大学科学封面)

Related housing price data: [HousingPrice]


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, Xuan Yang, Chunping Wang, Jintao Su and Xiaofeng Yang. Network Embedding via Motifs. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2021 (accepted).
  • 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]
  • 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]
  • 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]
  • 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 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.
  • 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 Scholarship
  • 2013, awarded Alumnus Scholarship of Tsinghua University
  • 2008-2009, ACM International Collegiate Programming Contest, Asia Regional, 5 gold medals
  • When not doing research, I clear my mind by playing Magic The Gathering (a world-wide trading card game). I won a Pro Tour Qualifier Tournament in 2014.
  • Ph.D. students
  • Jiarong Xu (5th year, co-advising)
  • Xuanwen Huang (2nd year)
  • Yifei Sun (2nd year)
  • Zhengzhe Fu (1st year)
  • Zhiqing Xiao (1st year)
  • Fanzhe Fu (1st year)
  • Lekui Zhou (graduated in 2020, work @ Huawei)
  • Master students
  • Jintao Su (2nd year)
  • Siqi You (2nd year)
  • Tao Yu (2nd year)
  • Junru Chen (1st year)
  • Xuan Yang (1st year)
  • Zhexuan Lu (1st year)
  • Teng Ke (1st year)
  • Donghong Cai (1st year)
  • Jiali Xie (1st year)
  • Shihao Tu (1st year)
  • Yihao Shang (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)
  • Undergraduate students
  • Ziwei Chai
  • Taoran Fang
  • Jiani Yang
  • Yingying Fan
  • Rui Feng (graduated in 2019, PhD @ Georgia Tech)
  • Tianqing Fang (graduated in 2019, PhD @ HKUST)
  • Zhanlin Sun (graduated in 2019, master @ CMU)