Topics in Network Data Analysis

Course Info

Instructor Facundo Mémoli, m e m o l i @ m a t h . o s u . e d u
Course Assistant Samir Chowdhury, c h o w d h u r y . 5 7 @ o s u . e d u
Course code CSE 5339 -- Spring 2016
Times: Tuesdays 1.50 -- 3.40 pm
Location: UH 0038.
Description: The emerging field of analysis of network analysis is another manifestation of the increasing availability of interesting data. Network data arises from applications in phylogenetics, social science, defence, commerce, neuroscience, and biology, to name a few sources. Networks are most often directed, in the sense that weights attached to edges do not satisfy any symmetry property, and this asymmetry often precludes the applicability of many standard methods for data analysis.

In this topics course we will go over recent literature about the subject and will:

- survey the state of the art regarding algorithms for extracting information from network datasets (such as clustering)

- survey different sources of interesting network data

Several possible research directions will be discussed.

Prerequisites: The course has minimal requisites: it is designed for students from Computer Science and Engineering, and Mathematics having knowledge of undergrad level math. Some knowledge of geometry will be useful, but not necessary. The course will provide the opportunity to explore different aspects of the material: interested students will have the opportunity of implementing some algorithms and/or exploring some research papers on different aspects of both the underlying mathematics and/or the algorithmic procedures.


Meeting 1 (1/12). First meeting. Introduction to the main ideas. Assigned readings: "Networks: a very short introduction".
Meeting 2 (1/19). (1) Selection of topics. (2) Samir: Networks in Biology and Neuroscience [slides].
Meeting 3 (1/26). (1) Jacob: Networks in Education, Psychology, and Linguistics [slides] (2) Facundo: the hippocampal network [slides].
Meeting 4 (2/2). Corey: Random networks and graphs. [slides]
Meeting 5 (2/9). (1) Willa: Epidemiology: Networks and Disease Spread. [slides] (2) Bowen: Networks based on words. [slides]
Meeting 6 (2/16). Dan: Analysis Algorithms for Large-Scale Networks. [slides]
Meeting 7 (2/23). Zane: Neural Networks. [slides]
Meeting 8 (3/1). Woojin: Coupling on-line and off-line random graph models. [slides]
Meeting 8 (3/8). Corey: Graphons and the cut distance. [slides] [ What is a graphon?]
Meeting 9 (3/22). Field Trip: We'll attend two MBI talks: Heather Harrington and Danielle Basset. Here's the information about the weeklong MBI workshop.
Meeting 10 (3/29). Jacob: Persistent Topology -- an introduction
Meeting 11 (4/5). Samir: Networks, markets and game theory. [slides] [Writeup]
Meeting 12 (4/12). Willa: Matlab demo about models for disease spread on networks. How the structure of the network gets injected into the model. [slides] [paper]
Meeting 13 (4/19).Bowen: Markov chains [slides]


Synergistic activities