Course Information:

  • IDS 595 - Social Computing PhD Seminar
  • Spring 2021
  • Mon. 3pm - 5:30pm
  • yuhenghu at uic dot edu
  • Office hour: Email


With the rise of various social media (Facebook, Twitter, Instagram, Snapchat, etc.), people can share content, opinions, insights, experiences, perspectives, and media themselves, as well as producing many new media. Social networks emerge with the pervasive use of social media. In this seminar, we will briefly introduce the background of social computing including concepts and principles such as small world, random networks, scale-free networks, laws, and distributions (normal distribution, Zipf’s law, power-law), search in networks, propagation and diffusion of influence, social recommendation, collective wisdom. We will learn what representative data collection techniques are, and how to use collected data to help achieve tasks such as recommendation, identifying key performance indicators, etc. We will study issues like public opinion, sentiment analysis, and reputation. This course aims to introduce the state-of-the-art developments in participatory social media techniques, social networks and analysis, network analysis and graph theory, link analysis, and social media mining, to study emerging problems, and to learn innovatively applying multidisciplinary approaches to problem-solving. The ultimate goal is to sharpen problem-solving and critical thinking skills of our graduate students and prepare them with this unique set of expertise for the increasing demands in IT industry and for in-depth advanced research.

Academic Integrity

You are expected to adhere to the highest standards of academic honesty. Unless otherwise specified, collaboration on assignments is not allowed. Use of published materials is allowed, but the sources should be explicitly stated in your solutions. Violations will be reviewed and sanctioned according to the University Policy on Academic Integrity. Collaborations among team members are only allowed for the final term projects that are selected. "Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students." For more information about violations of academic integrity and their consequences, consult

Weekly Schedule

Lecture 1 Intro to Social Computing
Lecture 2 Sociological Foundations I
Lecture 3 Sociological Foundations II
Lecture 4 Graph Theory
Lecture 5 Graph Theory and Network Measures
Lecture 6 Network Measures
Lecture 7 Network Models
Lecture 8 Network Models II
Lecture 9 Project Proposal Presentation
Lecture 10 Influence and Homophily I
Lecture 11 Influence and Homophily II
Lecture 12 Community Detection
Lecture 13 Graph Neural Networks
Lecture 14 Final Project Presentation