Goals
Individuals tend to interact with others of similar interests. In turn, their social interactions often influence their activities. Interest in understanding such relationships is rapidly increasing with the advent of online social media. Inferring complex relationships among many entities in a complex evolving system is also of interest in many information networks, including health and climate. This course introduces students to graph-based methods for analyzing and modeling the structures and dynamics of social and information network entities consisting of individuals and their connections. The course is structured to provide ample opportunity for participants to learn how groups function in large social and information networks. This practical course will allow students to scout around for promising social network analysis and modeling research topics through hands-on experience.
Prerequisites
Basic knowledge of programming, statistics, graph theory, and linear algebra.
Texts
Topics
- The small-world network models
- Centralized and decentralized social network search algorithms
- Power-laws and preferential attachment
- Diffusion and information propagation in social networks
- Influence maximization in social networks
- Community detection in social networks
- Models of network cascades
- Models of evolving social networks
- Link and attributes prediction
Grading
Homework (30%), midterm exam (20%), reading/presenting
assignments (20%), and an individual research project
for CIS5524 or a team project for CIS4524(30%).
Late Policy and Academic Honesty
An automatic extension of homework submission is acceptable, with a 20% daily penalty. Discussing materials with fellow students is acceptable, but programs, experiments, and reports must be completed individually.
Ph.D. Qualify Examination Eligibility
Elective for all CIS tracks: AI, IS, Computer and Network and Software Systems.