Computational Models for Social and Information Network Analysis Jie Tang (Tsinghua University) [Download]

In this tutorial, we will give a systematic introduction for mining and learning with social and information networks, covering topics ranging from individuals to dyadic interactions to group collectives to information space. We will start with our tutorial with real examples from WeChat to explain how online social networks influence our offline physical world. Then we will comprehensively introduce both the history and recent advances on social network analysis, including methodologies and algorithms for user profiling, user behavior modeling, social tie & group modeling, influence & information diffusion over structures. We will present how to apply computational models---graphical models, deep learning, and embedding techniques---to mining and learning with network structures. Uniquely, this tutorial aims to provide the audience with 1) underlying social and graph theories in social network analysis and 2) our experience in translating social network research into online & mobile applications in a couple of social networking sites, including Tencent WeChat and Games, Alibaba, XueTangX, AMiner, and Microsoft Academic. Finally, all the work introduced in the tutorial is guaranteed with open code and we will also take the opportunity to release the largest Open Academic Graph with 200 million nodes and 3 billion links for social network analysis.  

The outline is subject to change and update frequently [slides]

1. History of social and information network analysis 
   1. Interdisciplinary Research on Networks
   2. The emergence of computational social science
   3. CSS for social good
2. Social Influence in Networks
   1. Topical social influence
   2. Conformity influence
   3. Triadic and structural influence
   4. Social influence locality
   5. Deep learning for social influence
   6. Q&A
3. Representation Learning for Networks  
   1. Graph neural networks
   2. Network embedding review
   3. Network embedding theory
   4. Network embedding as matrix factorization
   5. Fast network embedding
   6. Q&A
4. Conclusion and Q&A (5 mins) 
   1. Summary 
   2. Open Data for Social and Information Network Analysis
   3. Q&A

Jie Tang is a Full Professor and the Vice Chair of with the Department of Computer Science and Technology at Tsinghua University, and was also visiting scholar at Cornell University, Hong Kong University of Science and Technology, and Southampton University.  His interests include social network analysis, data mining, and machine learning. He has published more than 200 journal/conference papers and holds 20 patents, attracting more than 10,000 citation counts. He serves as PC Co-Chair of CIKM'16, WSDM'15, ASONAM'15, SocInfo'12, Associate General Chair of KDD 2018, and Acting Editor-in-Chief of ACM TKDD, Associate Editors of IEEE TKDE/TBD and ACM TKDD/TIST. He leads the project AMiner.org for academic social network analysis and mining, which has attracted more than 8 million independent IP accesses from 220 countries/regions in the world. He was honored with the NSFC Distinguished Young Scholar, CCF Young Scientist Award, UK Royal Society-Newton Advanced Fellowship Award, Newton Advanced Scholarship Award, and ACM SIGKDD Service Award.

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. WWW'19. [PDF] [*data & code*]
2. Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. ProNE: Fast and Scalable Network Representation Learning. IJCAI'19. [PDF] [*data & code*]
3. Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Modeling Influence Locality in Large Social Networks. KDD’18. [PDF] [poster] [data & code] [video]
4. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM’18. (The most cited paper in WSDM’18 as of May 2019) [PDF] [Slides] [code]
5. Jiezhong Qiu, Jie Tang, Tracy Xiao Liu, Jie Gong, Chenhui Zhang, Qian Zhang, and Yufei Xue. Modeling and Predicting Learning Behavior in MOOCs. WSDM'16, pages 93-102. [PDF] [Code] [Data] [Related challenge--KDD CUP]
6. Jing Zhang, Jie Tang, Juanzi Li, Yang Liu, and Chunxiao Xing. Who Influenced You? Predicting Retweet via Social Influence Locality. TKDD, 2015. [PDF] [Code&Data]
7. Jing Zhang, Zhanpeng Fang, Wei Chen, and Jie Tang. Diffusion of "Following" Links in Microblogging Networks. IEEE Transaction on Knowledge and Data Engineering (TKDE), 2015, Volume 27, Issue 8, Pages 2093-2106. [PDF]
8. Jing Zhang, Biao Liu, Jie Tang, Ting Chen, and Juanzi Li. Social Influence Locality for Modeling Retweeting Behaviors. IJCAI’13, pages 2761-2767. [PDF] [Data&Code] [Poster]
9. Jie Tang, Sen Wu, and Jimeng Sun. Confluence: Conformity Influence in Large Social Networks. KDD'13, pages 347-355. [PDF] [Slides_PDF   Slides_PPT ] [Poster] [Data&Code] [System] [Video] (Full Presentation & Best Poster Award)
10. Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Social Influence Analysis in Large-scale Networks. KDD’09, pages 807-816. (Top 10 cited paper among all papers published at KDD in the past 10 years) [PDF] [Slides] [Data] [Code]
11. Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of Academic Social Networks. KDD'08, pages 990-998. (Top 3 cited papers among all KDD 2008's papers, More...) [PDF] [Slides] [System] [API] [Citation Data] [DBLP Citation Data] [Author-Conference-Topic (ACT) Model (source code)] [More Data]