Networks and statistics

Networks and statistics

URLs

StatNet

Leonid Zhukov: Network Analysis. Lecture 1. Introduction to Network Science
Leonid Zhukov: Network Analysis. Lecture 3. Random graphs.
https://www.youtube.com/results?search_query=random+network+model
Microsoft Research: The Phase Transition in Random Graphs: A Simple Proof
John Guttag: 4. Stochastic Thinking
John Guttag: 5. Random Walks
John Guttag: 6. Monte Carlo Simulation
Marco Brambilla: Social network statistics aggregator
https://www.youtube.com/results?search_query=statistical+network+analysis
Gesine Reinert: Statistical analysis of networks
Bitcoin Statistical Analysis Protocol And Network Trends (Part 2)
Lada Adamic: 2 2 2A Introduction to random graph models 1658
Jeff Picton: Markov Chain Monte Carlo and the Metropolis Alogorithm
Brendan O'Connor: Statistical Text Analysis for Social Science
Steve Horvath: Weighted Correlation Network Analysis & Systems Biologic Applications
Netlytic: Network Analysis Pt 1

Association analysis: Frequent Patterns, Support, Confidence and Association Rules

Natasa Milic-Frayling: Network analysis -- Why it matters... ALT-C 2012
Charles Weko: Numerical Analysis of Association Indices with SOCPROG

Derek Kane: Data Science - Part IV - Regression Analysis and ANOVA Concepts
Ludwig Schmidt: Linear Regression with Graph Constraints
Tselil Schramm: Graph Clustering Algorithms

16. Network Analysis
a. Prerequisites: none
An introduction to various concepts, methods, and applications of social network analysis drawn from the social and behavioral sciences. The primary focus of these methods is the analysis of relational data measured on groups of social actors. Topics to be discussed include a basic introduction to network analysis, graphs and matrices, basic network measures and visualization, reciprocity and transitivity, dyadic and triadic analysis, centrality, egocentric networks, two-mode networks (affiliations, bibliographic/scientometric analysis), cohesive subgroups, equivalences and blockmodeling, hubs & authorities, cores & peripheries, clustering and graph partitioning, large scale structure of networks, statistical modeling in network (ergm/p*/RSiena) and network dynamics, and change in networks.

17. Advanced Topics in Network Analysis
a. Prerequisites: introduction to network analysis or consent of the instructor
The conventional categorization of data analytic methods into descriptive and inferential statistics can be fruitfully applied to network analysis. Descriptive methods of network analysis are important for illuminating structural features of a given network, but they cannot be used to build and/or test theories about the generation of networks. Inferential methods of network analysis can be used to test hypotheses about the generation and evolution of a network, derive measures of uncertainty for network indices, and find probabilistic models that accurately describe the overall features of a network.

18. Network Analysis: Statistical Approaches and modeling
a. Prerequisites: introduction to network analysis or consent of the instructor
Advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), the formation of ties based on attributes (e.g. homophily), and network effects on individual attributes (social influence or contagion models). Statistical models (blockmodeling, diffusion, etc.)

19. Network Analysis: Application in R
a. Prerequisites: none
The focus of the course will be how to develop questions about social networks and appropriately test them using the R statistical programming language. Because it is critically important for researchers to be able to analyze the data, and standardized packages hardly ever offer the required set of analytic methods, we are faced with having to write our own code for analysis of specific datasets. Minimal programming skills are desirable, though not required.

1. Random

Random numbers
Distributions
Fitting
Monte Carlo

2. Basic models

Erdos-Renyi
Small worlds
Scale-free

3. Triads, motifs, graphlets

4. Random generation

Inductive classes

ZN transform

5. Statistics

stat; Padgett

Structural properties and attributes

Permutation test

Testing and Modeling Dependencies Between a Network and Nodal Attributes

Bailey K. Fosdick & Peter D. Hoff Pages 1047-1056 | Received 01 Feb 2014, Accepted author version posted online: 03 Feb 2015, Published online: 07 Nov 2015

QAP and MRQAP

The Quadratic Assignment Procedure (QAP) MRQAP Decker, Krackhardt, Snijders, “Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions”