CS 765 Complex Networks

Department of Computer Science & Engineering

UNR, Fall 2018

Course Information - Prerequisites - Objective - Description - Topics - Textbooks - Tools - Resources - Organization - Project - Grading - Schedule

Course Information

Class hours Monday & Wednesday, 2:30 - 3:45pm
Class location PE 102
Instructor Dr. Mehmet H Gunes
E-mail mgunes <at>unr.edu
Phone (775) 682-8313
Web page https://www.cse.unr.edu/~mgunes
Office SEM 216 (Scrugham Engineering-Mines)
Office hours Monday & Wednesday 12:30 - 2:00 pm or by appointment

You may look at earlier courses from Fall 2009, Fall 2010, Fall 2011, Spring 2013, Fall 2014, Fall 2016, and Fall 2017.



Students who successfully complete this course will gain:


Catalog Description: Theory and modeling: biological, information, social and technological networks. Network models: scale-free, small-world, power-law. Processes on networks: epidemics, resilience, search.

This course covers theory and modeling of real-world networks such as computer, social, and biological networks where the underlying topology is a dynamically growing complex graph.

Many phenomena in nature can be modeled as a network and studied using network science. Researchers from many areas including biology, computer science, engineering, epidemiology, mathematics, physics, and sociology have been studying complex networks of their field.

Scale-free networks and small-world networks are well known examples of complex networks where power-law degree distribution and high clustering are their respective characteristic feature. These networks have been identified in many fundamentally different systems. Complex networks display non-trivial topological features that require an in depth study.

Topics (Tentative)

Textbooks (Recommended)


Data Resources


Research Project

The main component of your grade is a research project in network science that may materialize as a paper. If your project has a significant computational component (e.g., downloading and analyzing a network dataset), then you may work with a partner after consulting with the instructor. The paper will be judged on the following criteria:

Following are sample project topics:

Grading (Tentative)

Both grading policy and scale are subject to change.

Grading Policy

Grading Scale

Important Note: You will have one week to appeal for your grades after the graded assignments/tests are returned. So, please keep this in mind if you think that there is a problem/issue with the grading of your work.

Schedule (Tentative), Notes & Assignments

This is a tentative schedule including the assignment dates. It is subject to readjustment depending on the time we actually spend in class covering the topics.

Date Lectures Assignments & Notes
Mon, Aug 27 Lecture #1: Introduction Connected: The Power of Six Degrees  
Wed, Aug 29 Lecture #2: Empirical Study of Networks  
Mon, Sep 3 Labor day (no class)  
Wed, Sep 5 Lecture #3: Mathematics of Networks  
Mon, Sep 10 Lecture #4: Network Metrics (components, path length, degree) Project Abstract due  
Wed, Sep 12 Lecture #5: Network Metrics (clustering, similarity, assortativity)  
Mon, Sep 17 Lecture #6: Centrality (degree, betweenness, closeness, eigenvector) Lab 1 due  
Wed, Sep 19 Lecture #7: Centrality (katz/bonacich, pagerank, hubs-authorities, lexrank)  
Mon, Sep 24 Lecture #8: Communities (node, group)  
Wed, Sep 26 Lecture #9: Communities (network, hierarchical) Lab 2 due  
Mon, Oct 1 Lecture #10: Network Models (regular, random)  
Wed, Oct 3 Lecture #11: Network Models (small world)  
Mon, Oct 8 Lecture #12: Network Models (scale free) Related Work report due  
Wed, Oct 10 Lecture #13: Network Evolution  
Mon, Oct 15 Lecture #14: Related work presentations (Ben, Farhan)  
Wed, Oct 17 Lecture #15: Related work presentations (Davut, Eric) Lab 3 due  
Mon, Oct 22 Lecture #16: Related work presentations (Aniruddha, Sui)  
Wed, Oct 24 Lecture #17: Related work presentations (Aavaas, Humphrey)  
Mon, Oct 29 Lecture #18: Data mining essentials  
Wed, Oct 31 Lecture #19: Data mining essentials Lab 4 due  
Mon, Nov 5 Lecture #20: Data mining essentials  
Wed, Nov 7 Lecture #21: Methodology presentations Methodology report due  
Mon, Nov 12 Lecture #22: Methodology presentations  
Wed, Nov 14 Lecture #23: Methodology presentations Paper critique due  
Mon, Nov 19 Lecture #24: Methodology presentations  
Wed, Nov 21 Lecture #25: Network dynamics  
Mon, Nov 26 Lecture #26: Network resilience Lab 5 due  
Wed, Nov 28 Lecture #27: Search in networks  
Mon, Dec 3 Lecture #28: Information diffusion  
Wed, Dec 5 Lecture #29: Temporal networks Lab 6 due  
Mon, Dec 10 Lecture #30: Graph Mining  
Wed, Dec 19 Final Project Presentations @ 12:10pm Final report due @ 8am  


Course Information - Prerequisites - Objective - Description - Topics - Textbooks - Tools - Resources - Organization - Project - Grading - Schedule

Last updated on Oct 10, 2018