The wide applicability of modeling and analyzing graph data, coupled with rapid growth in the amount of graph data to be analyzed has led to the development of various large-scale graph processing systems over past few years. Developing these systems requires careful design of fundamental components like graph data structures, concurrent execution models and programming models to maximize parallelism, improve resource utilization and support user-guided graph analytics. This course explores the emerging research in development of scalable graph processing systems. We will explore how challenges in processing large graphs are being solved in real-world systems as well as the limitations inherent in their designs. This is a seminar and project based course, meaning that students are expected to give presentations on research papers and also work towards a term project. Students will require background in parallel computing, operating systems and databases. [ Course Outline ]

GRADING
Presentation 40%, Project 40% (Bonus 20%), Class Participation 20%.

ACADEMIC HONESTY STATEMENT
Academic honesty plays a key role in our efforts to maintain a high standard of academic excellence and integrity. Students are advised that ALL acts of intellectual dishonesty will be handled in accordance with the SFU Academic Honesty and Student Conduct Policies (http://www.sfu.ca/policies/gazette/student.html).