Daniel Oehm wrote this interesting blog about how to simulate realistic data using a Bayesian network. Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph. Through these relationships, one…
Tag: network
Overviews of Graph Classification and Network Clustering methods
Thanks to Sebastian Raschka I am able to share this great GitHub overview page of relevant graph classification techniques, and the scientific papers behind them. The overview divides the algorithms into four groups: Factorization Spectral and Statistical Fingerprints Deep Learning Graph Kernels Moreover, the overview contains links to similar collections on community detection, classification/regression trees and gradient boosting papers…
Overview of built-in colors in R
Most of my data visualizations I create using R programming — as you might have noticed from the content of my website. Though I am colorblind myself, I love to work with colors and color palettes in my visualizations. And I’ve come across quite some neat tricks in my time. For instance, did you it’s…
Xenographics: Unusual charts and maps
Xeno.graphics is the collection of unusual charts and maps Maarten Lambrechts maintains. It’s a repository of novel, innovative, and experimental visualizations to inspire you, to fight xenographphobia, and popularize new chart types. For instance, have you ever before heard of a time curve? These are very useful to visualize the development of a relationship over time. The…
Evolving Floorplans – by Joel Simon
Joel Simon is the genius behind an experimental project exploring optimized school blueprints. Joel used graph-contraction and ant-colony pathing algorithms as growth processes, which could generate elementary school designs optimized for all kinds of characteristics: walking time, hallway usage, outdoor views, and escape routes just to name a few. Definitely check out the original write-up if you…
Interactive Explanation of Network and Graph Principles
Why do groups of people act smart, dumb, kind, or cruel? People behave in strange ways, particularly when they are able to influence one another. Both good and bad things can happen when people interact and behave in network structures. On the bright side, you must be familiar with the wisdom of the crowd, where…
Identifying “Dirty” Twitter Bots with R and Python
Past week, I came across two programming initiatives to uncover Twitter bots and one attempt to identify fake Instagram accounts. Mike Kearney developed the R package botornot which applies machine learning to estimate the probability that a Twitter user is a bot. His default model is a gradient boosted model trained using both users-level (bio, location, number of…
Harry Plotter: Network analysis of spell usage
Apparently, I was not the only geek who decided to celebrate the 20th anniversary of the Harry Potter saga with statistical analysis. Students Moritz Haine and Markus Dienstknecht of the Data Science for Decision Making Master at Maastricht University started their own celebratory project as part of a course Information Retrieval and Text Mining. Students in…
Sentiment Analysis of Stranger Things Seasons 1 and 2
Jordan Dworkin, a Biostatistics PhD student at the University of Pennsylvania, is one of the few million fans of Stranger Things, a 80s-themed Netflix series combining drama, fantasy, mystery, and horror. Awaiting the third season, Jordan was curious as to the emotional voyage viewers went through during the series, and he decided to examine this…
