Student Submissions
International Trade for Battery and Electric Network
Network Analysis of 10 years Trade Data for top producers of Copper, Lithium, and Graphite
Exploration of Brokerage, Centralization, Centrality, Authority, Hub, and Bridge of International Trade for Battery and Electric Network
Network Analysis of 10 years Trade Data for top producers of Copper, Lithium, and Graphite
Blockmodels and Strctural Equivalence of International Trade for Battery and Electric Network
Network Analysis of 10 years Trade Data for top producers of Copper, Lithium, and Graphite
Community Detection of International Trade for Battery and Electric Network
Network Analysis of 10 years Trade Data for top producers of Copper, Lithium, and Graphite
Community Detection
Detecting Communities in out Football Transfer Network
Assignment9
assignment for political and social network analysis
Network Analysis Exploration - Harry Potter Version
Catching Up in Networks Using the Harry Potter Network Dataset
Assignment_8
Network Statistics (Marriages between characters in the Game of Thrones Novels).
Clustering Stocks
A closer look into the S&P 500 stocks
Inferential Network Statistics
A closer look into the S&P 500 stock network properties.
The Human Disease -- Week 3
Gene to Gene Network Analysis (bird's eye view)
Introduction to Football Networks
In this blog, We'll be referring to soccer as football -- since it's played with a foot.
Network Degree
Network Degree of Football Networks
Exploring European Football Transfer Networks
Exploring a network of football teams and the transactions they made from 2018-2021. An Edgelist maybe?
Network Status and Centrality
Who are the key actors in the football network? What makes them the key actors
Introduction to Football Networks
In this blog, We'll be referring to soccer as football -- since it's played with a foot.
Statistical Analysis
Using Univariate Conditional Uniform Graph Tests
DACSS 697E Assignment 7
Assignment 7 for DACSS 697E course 'Social and Political Network Analysis': "Networks: Community"
DACSS 697E Assignment 6
Assignment 6 for DACSS 697E course 'Social and Political Network Analysis': "Network Roles"
DACSS 697E Assignment 5
Assignment 5 for DACSS 697E course 'Social and Political Network Analysis': "Brokerage and Betweenness"
DACSS 697E Assignment 4
Assignment 4 for DACSS 697E course 'Social and Political Network Analysis': "Status & Eigenvector Centrality"
DACSS 697E Assignment 3
Assignment 3 for DACSS 697E course 'Social and Political Network Analysis': "Grateful Research: Creating a Network"
Elitism of the Supreme Court: Liberals v. Conservatives
"After the feedback about this homework, I hopefully fixed my dataset. A tie now consists of a Justice and the school they attened and every school they have hired a clerk from. This solved my issue from before where there was limited connections. My results were chaotic involving the entire history of Justices. I decided to focus on the current Justices on the Supreme Court. Further, I thought it would be interesting to split up and compare networks of conservative and liberal Justices(based on the party of the appointing president). The Democrat Appointed Justices Network has 27 nodes, is directed, and has 243 total edges. The REpublican Appointed Justices Network has 35 nodes, is directed, and has 334 total edges."
Homework 9
Week 9 Assignment: Network Statistics.
Networks Blog Post 9
A short description of the post.
Elitism of the Supreme Court: Liberals v. Conservatives
"After the feedback about this homework, I hopefully fixed my dataset. A tie now consists of a Justice and the school they attened and every school they have hired a clerk from. This solved my issue from before where there was limited connections. My results were chaotic involving the entire history of Justices. I decided to focus on the current Justices on the Supreme Court. Further, I thought it would be interesting to split up and compare networks of conservative and liberal Justices(based on the party of the appointing president). The Democrat Appointed Justices Network has 27 nodes, is directed, and has 243 total edges. The REpublican Appointed Justices Network has 35 nodes, is directed, and has 334 total edges."
Elitism of the Supreme Court: Liberals v. Conservatives
"After the feedback about this homework, I hopefully fixed my dataset. A tie now consists of a Justice and the school they attened and every school they have hired a clerk from. This solved my issue from before where there was limited connections. My results were chaotic involving the entire history of Justices. I decided to focus on the current Justices on the Supreme Court. Further, I thought it would be interesting to split up and compare networks of conservative and liberal Justices(based on the party of the appointing president)."
Assignment_7
Community Detection (Marriages between characters in the Game of Thrones Novels)
Assignment_6
Roles and Blockmodels (Marriages between characters in the Game of Thrones Novels)
Community Detection
A comparison of community clusters in the IACtHR network using different algorithms
How Elite is the Supreme Court?
"After struggling most weeks trying to work with my dataset, I realized from our classes that I was looking at my dataset the wrong way. So, I decided to flip the format. I am now looking at just how elite the Supreme Court has been over its history. I do this by grouping Justices to the school they attended. It was such a relief to be able to run the different network commands and actually get graphs or data. I will save working on formating the graphs for the future when my brain recovers. The new version of my dataset has 104 vertices. It is a directed network. It is not bipartite and there are a total of 76 edges. This was my record for going the longest in R without running into a wall. I feel a lot more comfortable with R and using network analysis in R ,but I still have a lot of work to do when it comes to understanding the results."
Social Network Analysis Homework 7
Roles & Communities.
Assignment7
assignment for political and social network analysis
Blog Post 7, Integrating ML
This post is an analysis of community structure and machine learning techniques on my medieval dataset.
Into the 20th Century (Conflict Data) Homework 6
In this post I begin my analysis of the 20th century conflicts dataset.
Assignment6
assignment for political and social network analysis
How Elite is the Supreme Court?
"After struggling most weeks trying to work with my dataset, I realized from our classes that I was looking at my dataset the wrong way. So, I decided to flip the format. I am now looking at just how elite the Supreme Court has been over its history. I do this by grouping Justices to the school they attended. It was such a relief to be able to run the different network commands and actually get graphs or data. I will save working on formating the graphs for the future when my brain recovers. The new version of my dataset has 104 vertices. It is a directed network. It is not bipartite and there are a total of 76 edges."
Social Network Analysis Homework 6
Roles & blockmodels.
Week 5 Interpretaive Assignment
A short description of the post.
Betweeness Centrality
"My dataset includes every Supreme Court Justice and the school that their clerks attended. There are 187 vertices which contstitute the Justices and the different universiteis. There are 2487 edges and an edge means there is a connection between a Justice and a school because they have hired a clerk that graduated with their law degree from the university. I tried to calculate brokerage, but it says that my data is not proper and I get an error message. I am not sure if that it user error(most likely) or if I am just mixing the steps up. I was able to calcuate betweeness centrality, but I could not get dplyr to let me slice or arrange the data so I could tell which nodes were the highest. I will work further to make progress on working with the data."
Betweenness in the Amici Network
Brokerage, betweenness, and other centrality measures
Assignment_5
Structural Holes author: - name: Walid Medani url: https://walidmedani.github.io/networks-blog/
Homework 5
Week 5 Assignment: Brokerage and Power.
Week 2 Assignment
Analyzing the Enron Emails dataset from the network package
Initial Network Analysis Florentine Family
Homework 2: Brief Analysis of the Florentine Family Set
Florentine Families Week 3 Assignment
A short description of the post.
Work with Medieval Networks
A Brief Analysis of Networks of Medieval Conflict.
The Human Disease Taana Baana
Gene to Gene Network Analysis (bird's eye view)
Social Network Analysis: Week 2: Basic Network Structure
use `igraph` and `statnet` tools to describe aspects of network structure introduced in the Week 2 Lecture: Dyads and Dyad Census, Triads and Triad Census, Network Transitivity and Clustering, Path Length & Geodesic
Degree and Centrality
'My dataset includes every Supreme Court Justice and the school that their clerks attended. There are 187 vertices which contstitute the Justices and the different universiteis. There are 2487 edges and an edge means there is a connection between a Justice and a school because they have hired a clerk that graduated with their law degree from the university.The network density of the dataset is .149 not including loops. When looking at node degreee, you will see that Harvard has the highest count of relationships with 681, Yale is second wiht 464, and Chicago is third with 172.The median node degree is 3 and The mean is 27.33. While the max is 681. The centralziation score for both in and out degrees is 3.65. The nodes with the most outdegree are Harvard, Yale, Chicago, Standford, and Columbia. The nodes with the least outdegree are Penn, Northeastern, Virginia, Temple, Washington & Lee.'
Week 4
My dataset includes every Supreme Court Justice and the school that their clerks attended. There are 187 vertices which contstitute the Justices and the different universiteis. There are 2487 edges and an edge means there is a connection between a Justice and a school because they have hired a clerk that graduated with their law degree from the university. The centralization score for the dataset is .952. The more modern Justices have a higher betweenness score and I believe that is attributed to the fact that the total number of clerks have significantly grown starting back in the 1950s. The schools with the highest scores are Harvard, Georgetown, GW all schools where there have been many clerks hired from. While the schools with a few or only one clerk hired from have much lower scores. When it comes to eigenvector centarlity, shows some intersting reults. A modern justice such as Justice Gorsuch has among the highest while Justice Scalia is more middle of the pack. Howver, an older justice, Justice William Howard Taft has just as high of a score as Justice Gorsuch. I am not really sure what these results man. Justice Gorsuch's high score might be explained because he both clerked for a Justice and is a Justice himself. The school with the highest bonachi-power is Minnesota with -3.63 whil the lowest is Notre Dame. The Justice with the highest score is interestingly the newest Justice, Amy Coney-Barrett.
Testing
A new article created using the Distill format.
Short Assignment 2
Describing Network Data
Week 2 Assignment
An exploration of the Sampson's Monks dataset.
Week 4 Data Exploration
An exploration of centrality and centralization in the Florentine Families dataset
Networks Hw 2
A closer look at Enrons Emails
Short Assignment 2
A new article created using the Distill format.
Describing the Network Data
From raw data to network data
Status and Centrality Measures in the IAcHR Network
A look at the status measures of the network
Assignment_2
Network of militarized interstate disputes from 1870 to 2014. (https://correlatesofwar.org/data-sets/MIDs)
Assignment_3
Degree and Centrality. (https://correlatesofwar.org/data-sets/MIDs)
Assignment_4
Status and Eigenvector (https://correlatesofwar.org/data-sets/MIDs)
Homework 3
Week 3 Assignment: Degree and Centrality.
Homework 4
Week 4 Assignment: Network Status.
Welcome to DACSS 601
Welcome to DACSS 601: Foundations of Data Science. We hope you enjoy reading what we have to say!
