Professor: Boleslaw Szymanski — boleslaw.szymanski@gmail.com
Lectures: Mon & Thu 12pm - 1:40pm, J-ROWL 2C25
Office Houres: Tue 14:00 - 15:00, Thu 10:30am - 11:30am, MRC 335A
Textbook: Albert Laszlo Barabasi Network Science, Cambridge University Press, 2016
Description: This course offers the introduction to network science and review of current research in this area. Classes will interchangeably present chapters from the textbook and the related current research. The emphasis will be on mathematical background of network science: graphs and networks; random networks and various types of scale-free networks; network properties such as assortatitivity, mobility, robustness, social networks and communities; and dynamics of spreading in networks.
This is a web page for the class which contains the basic information about the course, lecture notes, This page will also contain the corrections and general news about the course.
Course Content includes: (i) Mathematical background of network science: graphs and networks, (ii) Random networks and their properties, (iii) The scale-free property, small world networks and Barabasi-Alert model, (iv) Evolving networks, (v) Degree Correlation (vi) Network robustness, (vii) Social networks and communities, and (viii) Spreading phenomena.
12-12:50pm Introduction and Overview: Six Degree of separation see https://youtu.be/zK1Cb9qj3qQ
16-16:50pm Community structure in complex networks, Prof. Santo Fortunato, Indiana University
Graph Theory (pdf) (textbook chapter 2)
Graph Theory (slides) (textbook chapter 2)
Assignment 1 due October 30, 2016 noon, contacts szymab@rpi.edu
Dominating Sets, Dr. Noemi Derzsy (slides)
Dominating Sets, Dr. Noemi Derzsy (pdf)
Evolution Dynamics in social networks, (slides) Ashwin Bahulkar
Evolution Dynamics in social networks (pdf), Ashwin Bahulkar
BA Model part I (slides) (textbook chapter 5)
BA Model part I (pdf) (textbook chapter 5)
Assignment 1: Synthetic Networks due October 30, 2016
BA Model part II (slides) (textbook chapter 5)
BA Model part II (pdf) (textbook chapter 5)
Robustness II (slides) (textbook chapter 8)
Robustness II (pdf) (textbook chapter 8)
Quantifying Long-Term Scientific Impact, Ian Gross
Greedy Algorithm for Community Detection, Matthew Mohr (slides)
Greedy Algorithm for Community Detection, Matthew Mohr
A New Type of Neurons for Machine Learning, Fenglei Fan (slides)
Beyond the Degree Distribution, Miao Qi (slides)
Beyond the Degree Distribution, Miao Qi
Research: WEF Global Risk Model, Alaa Mousawi (slides)
Research: WEF Global Risk Model, Alaa Mousawi
Research: Power Grids, Alaa Mousawi (slides)
Research: Power Grids, Alaa Mousawi
Research: Risk Evolution, Xiang Niu (slides)
Research: Risk Evolution, Xiang Niu
Research: Risk Evolution, Xiang Niu (slies)
Research: Risk Evolution, Xiang Niu
Network Analysis of Convertible Debt Systemic Risk, Yueliang Lu
Link Prediction on Hacker Networks, Samantha Lee (slides)
Link Prediction on Hacker Networks, Samantha Lee
Agglomerative vs. Divisive Community Detection, Jacob Fucci (slides)
Agglomerative vs. Divisive Community Detection, Jacob Fucci
Partisan bubbles: Political bias of Reddit Users, Diego Cepeda
Measuring fitness of evolving networks, Mauricio Gouva (slides)
Milgram Experiment, Shruthi Chari (slides)
Milgram Experiment, Shruthi Chari
Quantifying the evolution of individual scientific impact, Tommy Fang (slides)
Quantifying the evolution of individual scientific impact, Tommy Fang
Integrating Supply Chain and Network Analysis, Yue Yin (slides)