Computational Models for Social and Information Network Analysis Jie Tang (Tsinghua University) & Yuxiao Dong (Microsoft Research) join discussion group here

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 

1. Part 0: History of social and information network analysis (35 minutes) [Slides] 
   1. Interdisciplinary Research on Networks
   2. Classical mining & learning problems in networks
   3. Overview of the tutorial 
   4. Open data for network analysis  
   5. Q&A
2. User Profiling and Modeling (45 mins) [Slides]
   1. User Demographics
   2. User Profiling across Networks
   3. Deep Learning for User Profiling
   4. Q&A
3. S​ocial Tie and Social Influence (45 mins) [Slides]
   1. Social Tie
   2. Topic Influence
   3. Triadic Influence
   4. Structural Influence
   5. Deep Learning for Social Influence
   6. Q&A
4. Representation Learning for Networks (75 mins) [Slides]
   1. Homogeneous Network Embedding
   2. Heterogeneous Network Embedding
   3. Network Embedding Theory
   4. Deep Convolution and Attention Networks for Embedding
   5. GAN for network embeddings
   6. Network Embedding Applications
   7. Q&A
5. Conclusion and Q&A (5 mins) [Slides]
   1. Summary 
   2. Open Data for Social and Information Network Analysis
   3. Q&A

Jie Tang is an Associate Professor 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 Organizing Co-Chairs of KDD'11-18, 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, Newton Advanced Scholarship Award, and CCF Young Scientist Award.

Yuxiao Dong is a Senior Applied Scientist at Microsoft Research, Redmond. He received his Ph.D. from University of Notre Dame and has been a visiting scholar at Tsinghua University, U.S. Army Research Lab, and AMiner.org. His research focuses on data mining, network science, and computational social science, with an emphasis on applying computational models to addressing problems in large-scale networked systems, such as the Microsoft Academic Graph (MAG), mobile communication, and online social media. His work has been mainly published in KDD and interdisciplinary journals, winning four best paper awards/nominations as well as the 2017 ACM SIGKDD Doctoral Dissertation Award Honorable Mention.