工學(xué)博士 (計算機(jī)科學(xué)與技術(shù)), 清華大學(xué), 中國, 2006.

人工智能

數(shù)據(jù)挖掘

社交網(wǎng)絡(luò)

機(jī)器學(xué)習(xí)

知識圖譜

研究興趣主要包括人工智能、數(shù)據(jù)挖掘、社交網(wǎng)絡(luò)、機(jī)器學(xué)習(xí)以及知識圖譜。

主要創(chuàng)新性研究包括：

1）社會影響力分析：提出基于話題的社會網(wǎng)絡(luò)影響力模型，針對大規(guī)模社會網(wǎng)絡(luò)進(jìn)行用戶級別的微觀建模，自動計算用戶之間基于不同話題層次的影響力強(qiáng)度，為定量化、細(xì)粒度的網(wǎng)絡(luò)影響力分析給出理論基礎(chǔ)，部分解決了影響力最大傳播模型的輸入假設(shè)問題。

2）社會網(wǎng)絡(luò)用戶行為建模：將社會網(wǎng)絡(luò)的基礎(chǔ)理論(結(jié)構(gòu)平衡理論、兩階段傳播理論、結(jié)構(gòu)洞理論等) 融入概率因子圖模型中對社會網(wǎng)絡(luò)關(guān)系和強(qiáng)度進(jìn)行定量描述，實現(xiàn)了社會網(wǎng)絡(luò)關(guān)系挖掘的統(tǒng)一學(xué)習(xí)算法。

3）網(wǎng)絡(luò)行為建模和影響力分析，提出了針對社會網(wǎng)絡(luò)的微觀動態(tài)分析方法，并首次提出了社會影響力的量化分析方法，以及社會網(wǎng)絡(luò)行為和社會影響力關(guān)聯(lián)關(guān)系的分析方法。

4）應(yīng)用上述研究成果，研發(fā)了完全自主知識產(chǎn)權(quán)的科技情報大數(shù)據(jù)挖掘與服務(wù)平臺AMiner。系統(tǒng)2006年上線以來，吸引了來自全球220個國家/地區(qū)的1000多萬次獨(dú)立IP訪問。

國家自然科學(xué)基金課題: 統(tǒng)一的語義內(nèi)容標(biāo)注模型研究 (2008-2010);

國家自然科學(xué)基金重點(diǎn)課題: 面向Web的社會網(wǎng)絡(luò)理論與方法研究 (2010-2013);

863課題: 基于概率圖模型的異構(gòu)XML數(shù)據(jù)集成與檢索 (2009-2010);

IBM國際合作項目: 社會網(wǎng)絡(luò)搜索和挖掘 (2007-2011);

Nokia國際合作項目: 基于移動終端的本體場景建模和管理 (2009-2011).

[1] Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. In Proceedings of the Web Conference 2019 (WWW’19) (accepted).

[2] Yukuo Cen, Jing Zhang, Gaofei Wang, Yujie Qian, Chuizheng Meng, Zonghong Dai, Hongxia Yang, and Jie Tang. Trust Relationship Prediction in Alibaba E-Commerce Platform. IEEE Transaction on Knowledge and Data Engineering (TKDE), 2019 (accepted).

[3] Wenzheng Feng, Jie Tang, Tracy Xiao Liu, Shuhuai Zhang, and Jian Guan. Understanding Dropouts in MOOCs. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19).

[4] Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, and Maosong Sun. Bandit Learning with Implicit Feedback. In Proceedings of the Thirty-Second Annual Conference on Neural Information Processing Systems (NIPS’18).

[5] Yutao Zhang, Fanjin Zhang, Peiran Yao, and Jie Tang. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. In Proceedings of KDDu201918, pages 1002-1011.

[6] Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Social Influence Prediction with Deep Learning. In Proceedings of the Twenty-Forth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18).

[7] Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, and Xiaoming Fu . Will Triadic Closure Strengthen Ties in Social Networks? ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, Volume 12 Issue 3, Article No. 30.

[8] Yang Yang, Jie Tang, and Juanzi Li. Learning to Infer Competitive Relationships in Heterogeneous Networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, Volume 12 Issue 1, Article No. 12

[9] Yujie Qian, Jie Tang, and Kan Wu. Weakly Learning to Match Experts in Online Community. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

[10] Kan Wu, Jie Tang, and Chenhui Zhang. Where have you been? Inferring career trajectory from academic social network. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

[11] Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, Tatseng Chua, Thanassis Tiropanis, and Wendy Hall. Cross-Domain Depression Detection via Harvesting Social Media. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

[12] Xiaotao Gu, Hong Yang, Jie Tang, Jing Zhang, Fanjin Zhang, Debing Liu, Wendy Hall, and Xiao Fu. Profiling Web Users Using Big Data. Social Network Analysis and Mining (SNAM), accepted.

[13] Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, and Xiaoming Fu. Will Triadic Closure Strengthen Ties in Social Networks? ACM Transactions on Knowledge Discovery from Data (TKDD), accepted.

[14] Yutao Zhang, Robert Chen, Jie Tang, Jimeng Sun, and Walter Stewart. LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity. In Proceedings of the Twenty-Third ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17), pages 1315-1324.

[15] Jing Zhang, Jie Tang, Yuanyi Zhong, Yuchen Mo, Juanzi Li, Guojie Song, Wendy Hall, and Jimeng Sun. StructInf: Mining Structural Influence from Social Streams. In Proceedings of AAAI’17, pages 73-79.

[16] Jie Tang. Computational Models for Social Network Analysis: A Brief Survey. In Proceedings of WWW’17, pages 921-925.

[17] Yu Han, Jie Tang, Hao Ye, and Bo Chen. Who to Invite Next? Predicting Invitees of Social Groups. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17), pages 921-925.

[18] Liangming Pan, Chengjiang Li, Juanzi Li, and Jie Tang. Prerequisite Relation Learning for Concepts in MOOCs. In Proceedings of the 55th Annual Meeting of the Association of Computational Linguistics (ACL’17), pages 1447-1456.

[19] Jie Tang. Computational Models for Social Network Analysis: A Brief Survey. In Proceedings of the Twenty-Sixth World Wide Web Conference (WWW’17), pages 921-925.

[20] Jie Tang and Wendy Hal. Cross-domain Ranking via Latent Space Learning. In Proceedings of AAAI’17.

ACM TKDD執(zhí)行主編；

IEEE TKDE、 ACM TIST、 IEEE TBD、 Science China編委；

Web Intelligence 2010: 程序委員會副主席 (2010);

KDD-LDMTA 2010, ICDM-LDMTA 2009-2010, WWW-SWSM 2008, CIKM-SWSM 2009: 聯(lián)合主席 (2008-2010);

KDD 2010, SIGIR 2009-2010, WWW 2010, ACL 2010, COLING 2010: 程序委員會委員 (2009-2010).