Jie Tang (Tang, Jie)

Associate Professor, IEEE Senior Member, ACM Senior Member, CCF Distinguished Member.

Knowledge Engineering Lab (Group)
Department of Computer Science and Technology
Tsinghua University

Work Phone Number:  +8610-62788788-20
Office:  1-308, FIT Building, Tsinghua University, Beijing, 100084. China PR.
E-Mail:  jietang at tsinghua . edu . cn
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Research           Publications           Academic Activities           System           Awards           Invited Talks           Students          

I am an associate professor in Department of Computer Science and Technology of Tsinghua University. I obtained my Ph.D. in DCST of Tsinghua University in 2006. I became ACM Professional member in 2006, IEEE member in 2007, and CCF Distinguished Member in 2015. My research interests include social network theories, data mining methodologies, machine learning algorithms, and semantic web technologies.

I have been visiting scholar at Cornell University (working with John Hopcroft), University of Illinois at Urbana-Champaign (short term, working with Jiawei Han), Chinese University of Hong Kong (working with Jeffrey Yu), and Hong Kong University of Science and Technology (working with Qiong Luo). During my graduate career, I have been an intern at NLC group of Microsoft Research Asia from 2004 to 2005. I also have attended the internship program of IBM China Research Lab in 2004.

New * Keynote on Can AI help MOOC? at EDM 2017! [Slides Download] [PDF Version]
New * Tutorial on WWW 2017: Computational Models for Social Network Analysis.
New * Keynote on BIG Network Analysis--Theories, Algorithms, and Applications at ICDM 2016 Workshop on Data Science for Social Media and Risk (SOMERIS'16)! [Slides Download] [PDF Version]
New * Invited talk AMiner (ArnetMiner)--Toward understanding big scholar data at Data Science of China Session at KDD 2016! [Slides Download] [PDF Version]
New * Keynote Social Influence and Sentiment Analysis at KDD 2016 Workshop on Sentiment Discovery and Opinion Mining (WISDOM'16)! [Slides Download] [PDF Version]
New * Keynote AMiner (ArnetMiner)--Toward understanding big scholar data at WWW Big Scholar Workshop'16! [Slides Download] [PDF Version]
New * Invited Talk AMiner (ArnetMiner)--Toward understanding big scholar data at WSDM'16! [Slides Download] [PDF Version]
New * A new tutorial about Computational Models for Social Network Analysis --User Modeling, Social Tie, and Group Formation. [Slides Download] [PDF Version]

New * I am looking for highly-motivated students to work with me on the exciting area of social network, data mining, and machine learning.
New * I also have a few open Postdoctoral Positions to investigate underlying theory and algorithms in data mining, social network analysis, and machine learning.
New * If you want me to write a recommendation letter for you, please first read this.

New * Arnetminer.org becomes a Sponsor of SIGKDD 2012, SIGKDD 2011, ICDM 2011, ECML/PKDD 2011, WSDM 2011, ICMLA 2011, ICTAI 2011.
New * PatentMiner.org is online. It is a general topic-driven framework for analyzing and mining heterogeneous patent networks. Relevant papers have been published at KDD'12.
New * Recipient of Newton Advanced Fellowship Award, 2012 CCF Young Scientist Award, 2012 NSFC Excellent Young Scholar, 2012 Best employee of DCST of Tsinghua University, 2012 SIGKDD Best Poster Award, 2012 JCDL Best Student Paper Nomination,

An overview of the recent research in our laboratory: Computational Models for Micro-level Social Network Analysis ([PDF Version])

* Social Influence
Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. There are three major research topics in social influence: test, measure, and model.
    Our research mainly focuses on quantifying the influential strength between users in large social networks. We try to answer several challenging questions: (1) How to differentiate the social influences from different angles(topics)? (2) How to quantify the strength of those social influences? (3) How to estimate the model on real large networks?
    We propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks (Tang et al., KDD'09) and investigate a new problem of conformity influence analysis (Tang et al., KDD'13). We also study the conservative and non-conservative influence propagation over heterogeneous networks (Liu et al., DMKD'12) and propose the notion of social influence locality for modeling retweeting behaviors (Zhang et al., IJCAI'13). We further propose a NTT-FGM model to formalize social influence, correlation (homophily), and users' action dependency into a unified approach and distinguish their effects for modeling and predicting users' actions in social networks (Tan et al., KDD'10). And apply social influence for analyzing user-level sentiment in social networks (Tan et al., KDD'11).

Related data sets and codes: [Topic-Influence]  [Influence-over-Heterogeneous-Networks]  [Social-Action-Tracking]
Tutorials are given at WWW'14, WSDM'13 and ASONAM'12, and can be downloaded here [Slides] [PDF].
A survey of models and algorithms for social influence analysis can be found here.
* Structural Holes & Information Diffusion
The theory of structural holes suggests that individuals would benefit from filling the ``holes'' (called as structural hole spanners) between people or groups that are otherwise disconnected.
    The fundamental challenge we want to address is to detect users who span structural holes in social networks and how the structural hole spanners influence the information diffusion?
    We explore the problem of mining structural hole spanners through information diffusion in social networks (Lou and Tang, WWW'13). We precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models (HIS and MaxD) have been developed to implement the objective function. The optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees.
Related data sets and codes: [Structural hole&Information diffusion]
* Social Tie & Community
In online social networks, social relationship is the most basic unit to form the network structure. Relationships between users can be either directed or undirected.
    We focus on studying two aspects of social tie: (1) to which extent the label of social ties between people can be inferred in social networks? (2) how reciprocal (two-way) relationships are developed from parasocial (one-way) relationships and how the relationships further develop into triadic closure and communities? (3) how communities dynamically evolve over time?
    We propose a unsupervised dynamic factor graph model to infer advisor-advisee relationship from the coauthor network (Wang et al., KDD'10) and a partially labeled factor graph model to infer the type of social relationships (Tang et al., ECML/PKDD'11, PKDD Best Student Paper Runnerup). We further incoporate social theories (e.g., social balance theory, social status theory, structural hole theory, two-step flow theory, and strong/weak tie) into a triad-based factor graph model to infer the formation of reciprocal relationships from parasocial relationships (Hopcroft et al., CIKM'11) and to infer the formation of triad closure (Lou et al., TKDD'13), and leverage features defined based on those social theories to infer social ties across heterogeneous networks (Tang et al., WSDM'12). We have further proposed a co-evolution model for modeling the dynamic changes of communities in social networks (Sun et al., TKDE'13).

Related data sets and codes: [Social-Tie]  [Reciprocity&Triadic Closure]
Invited talks were given at different venues and related slides can be downloaded here. [PDF]
* Factor Graph Models
Factor graph is one type of probabilistic graphical models, providing an elegant way to represent both undirected graphical structure and directed graphical structure, with more emphasis on the factorization of the distribution.
    Online social networks are getting larger and machine learning tasks are facing several challenges: (1) labeled data is insufficient and how to leverage the unlabeled data for learning a graphical model? (2) how to leverage the correlation and the network information to help build the graphical model?
    We design two categories of factor graph models. The first category is for unsupervised learning. We have proposed Topical Factor Graph (TFG) (Tang et al., KDD'09), Time-constrained Probabilistic Factor Graph model (TPFG) (Wang et al., KDD'10). The second category is for supervised learning. We have proposed Partially Labeled Factor Graph model (PLFG) (Zhuang et al., DMKD'12), Triad-based Factor Graph model (TriFG) (Lou et al., TKDD'12), Transfer-based Factor Graph model (TranFG) (Tang et al., WSDM'12).

Related codes: [Partially Labeled Factor Graph  readme. Refer to Web page or Tang et al. PKDD'11 for details.]
[Topic Affinity Propagation. Refer to Web Page or Tang et al. KDD'09 for details.]

Other Research Topics: we are also applying the studied social network theories and data mining/machine learning algorithms to applications such as Social Recommendation (Tang et al., KDD'12, Wu et al., WSDM'13, Cai et al., TKDE'13), Emotion Prediction (Tang et al., TAC'12, IEEE TAC spotlight paper; Jia et al., Multimedia'12), Informatin Integration (Zhong et al., SIGMOD'09, Wang et al., WWW'12, Tang et al., TKDE'12, Wang et al., IJCAI'13), Social Context Summarization (Yang et al., SIGIR'11), Social Content Alignment (Hou et al., IJCAI'13).

Notable Systems:


Social Network Theories/Data Mining

Semantic Web/Database






Advising   Go Top

Post Doc

PhD Students

Master Students

Undergraduate Students (working with me for at least 8 months. I may miss some. Please let me know if you found.)

Other students collaborated with me

Last updated date: March 26, 2015, by Jie Tang.