Introduction to Network Science: graphs, dynamics, and randomized algorithms
ECE 592 / CSC 591 - Section 069, Fall 2017

 

Short Description:

Many modern complex networks such as Internet of Things, Online Social Networks, large scale wireless ad-hoc networks, power-grid, biological networks, etc. can be represented as a (large) graph. This course will first provide an introduction to graph modeling, its characterization, metrics on graph, ranking, and how to describe popular dynamics on graphs including random walk and epidemics. It also covers basics of graph sampling, property estimation, and randomized algorithms for computationally prohibitive (NP-hard) tasks such as clustering/partitioning. Applications include Google Pagerank algorithm, graph sampling, property estimation, identifying/ranking nodes, security, with connections to various statistical methods in machine learning and big (graph) data.

Time and Place

11:45AM -- 1:00 PM:  Tue &Thurs., 1229 EB2

Instructor

Do Young Eun
Office: 3064 EBII
Phone: 919-513-7406

Office Hours:   Tue. 2--3PM & Thurs 10:30--11:30 AM (or by appointment) in 3064 EB2

Email: dyeun at ncsu dot edu

Prerequisites: Some background on linear algebra and basic probability will be helpful but not strictly required. Necessary background materials will be covered in class.

Note: Most lectures will be based on lecture notes and some of the references below

References: (to be added more)

Tentative structure:

  1. modeling, representation, classification, adjacency matrix, graph Laplacian
  2. metrics on graph: degree, centrality, algebraic connectivity, etc.
  3. random graph model, small-world graph, power-law, preferential attachment models
  4. extension: multi-layer graphs, dynamic graphs
  1. random walk, Google pagerank walk
  2. epidemics modeling (branching process, SIS, SIR, etc.)
  3. epidemics on general graph for information/malware spreading
  1. randomized algorithms with brief intro to MCMC (Markov chain Monte Carlo)
  2. graph sampling, estimation
  3. MCMC approach to NP-hard optimization problem, Simulated Annealing
  4. Graph clustering/partitioning, community detection, node ranking, gossip algorithms, etc.

Note:

Students with disabilities

Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students. See https://dso.dasa.ncsu.edu/ for more information. more information on NC State's policy on working with students with disabilities, please see http://policies.ncsu.edu/regulation/reg-02-20-01

Academic integrity

All the provisions of the NC State University's Code of Student Conduct and University Policy on Academic Integrity apply to this course. In addition, it is my understanding and expectation that your signature on any test or assignment means that you neither gave nor received unauthorized aid.