CIS 4524/5524: ANALYSIS AND MODELING OF SOCIAL AND INFORMATION NETWORKS
Spring 2023
Time: Thursday, 5:30-8:00pm, Tuttleman 302
Office
hours: Thursday 3:00-4:00pm
and by appointment
Goals:
Individuals have a tendency 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. The objective of this course
is to introduce students to graph-based methods for analyzing and modeling the
structures and dynamics of social and information network entities consisting
of individuals and the connections among them. The course is structured to
provide ample opportunity for participants to learn how groups function in
large social and information networks. This will be a practical course that
will allow students to scout around for promising social network analysis and
modeling research topics by a hands-on experience.
Prerequisites:
Basic knowledge in programming skills; statistics, graph theory,
and linear algebra.
Texts:
_
Barabasi, A-L Networks
Science, Cambridge 2016
_
Easley,
D. and Kleinberg, J. Networks, Crowds,
and Markets: Reasoning About a Highly Connected World Cambridge,
2010.
Topics:
Content
will include methods for analyzing and modeling the following aspects of social
networks:
_
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
20% penalty per day. 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.