Map of Best Breweries in America

clicar na imagem para seguir o link

Um mapa com as melhores produtoras de cerveja artesanal nos EUA e uma rota otimizada com algoritmos genéticos

RateBeer puts out a list every year for top 100 breweries in the world. The rankings are based on reviews, range across styles, and historical performance (and maybe a bit of subjectivity). RateBeer just published the list for 2018. Here’s a map of the 73 U.S.-based breweries.

Brewery Road Trip, Optimized With Genetic Algorithm

Now that we know where they are, let’s find out how to visit all of them in one go.

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norsecorp cyber attack info

clique na imagem para seguir o link

clique na imagem para seguir o link

Um site dinâmico q mapeia informação sobre ciberataques em tempo real

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Markov Chains explained visually

Boa forma de perceber como funcionam as cadeias de Markov

Boa forma de perceber como funcionam as cadeias de Markov

Adding on to their series of graphics to explain statistical concepts, Victor Powell and Lewis Lehe use a set of interactives to describe Markov Chains. Even if you already know what Markov Chains are or use them regularly, you can use the full-screen version to enter your own set of transition probabilities. Then let the simulation run.

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9 “must read” articles on Big Data

Textos para big data

Textos para big data

My selection

(*) I disagree with this Harvard Business Review author. Senior data scientists work on high level data from various sources, use automated processes for EDA (exploratory analysis) and spend little to no time in tedious, routine, mundane tasks (less than 5% of my time, in my case). I also use robust techniques that work well on relatively dirty data, and … I create and design the data myself in many cases.

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Introduction to social network methods

Um bom livro online sobre SNA

Um bom livro online sobre SNA

Robert A. Hanneman and Mark Riddle

Introduction to social network methods

Table of contents


About this book

This on-line textbook introduces many of the basics of formal approaches to the analysis of social networks.  The text relies heavily on the work of Freeman, Borgatti, and Everett (the authors of the UCINET software package). The materials here, and their organization, were also very strongly influenced by the text of Wasserman and Faust, and by a graduate seminar conducted by Professor Phillip Bonacich at UCLA.  Many other users have also made very helpful comments and suggestions based on the first version.   Errors and omissions, of course, are the responsibility of the authors.

You are invited to use and redistribute this text freely — but please acknowledge the source.

Hanneman, Robert A. and Mark Riddle.  2005.  Introduction to social network methods. Riverside, CA:  University of California, Riverside ( published in digital form athttp://faculty.ucr.edu/~hanneman/ )


Table of contents:

Preface
1.    Social network data
2.    Why formal methods?
3.    Using graphs to represent social relations
4.    Working with Netdraw to visualize graphs
5.    Using matrices to represent social relations
6.    Working with network data
7.    Connection
8.    Embedding
9.    Ego networks
10.  Centrality and power
11.  Cliques and sub-groups
12.  Positions and roles: The idea of equivalence
13.  Measures of similarity and structural equivalence
14.  Automorphic equivalence
15.  Regular equivalence
16.  Multiplex networks
17. Two-mode networks
18.  Some statistical tools
After word

Bibliography

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IFORS Network_Flow_Problems

Algoritmos e Problemas de Redes

Algoritmos e Problemas de Redes

The following 10 pages are in this category, out of 10 total.

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Lexical Distance Among the Languages of Europe

Um bom exemplo de aplicação de redes como visualização à proximidade das linguas Europeias

Um bom exemplo de aplicação de redes como visualização à proximidade das linguas Europeias

This chart shows the lexical distance — that is, the degree of overall vocabulary divergence — among the major languages of Europe.

The size of each circle represents the number of speakers for that language. Circles of the same color belong to the same language group. All the groups except for Finno-Ugric (in yellow) are in turn members of the Indo-European language family.

English is a member of the Germanic group (blue) within the Indo-European family. But thanks to 1066, William of Normandy, and all that, about 75% of the modern English vocabulary comes from French and Latin (ie the Romance languages, in orange) rather than Germanic sources. As a result, English (a Germanic language) and French (a Romance language) are actually closer to each other in lexical terms than Romanian (a Romance language) and French.

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Century of rock history

Para quem gosta de música esta visualização é extraordinária

Para quem gosta de música esta visualização é extraordinária

Jessica Edmondson visualized the history of rock music, from foundations in the pre-1900s to a boom in the 1960s and finally to what we have now. Nodes represent music styles, and edges represent musical connections. There are a lot of them and as a whole it’s a screen of spaghetti, but it’s animated, which is key. It starts at the beginning and develops over time, so you know where to go and what to look at. Music samples for each genre is also a nice touch. [Thanks, Jessica]

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Using Metadata to Find Paul Revere

Um exemplo de um estudo de redes sociais

Um exemplo de um estudo de redes sociais

It’s just metadata. What can you do with that? Kieran Healy, a sociology professor at Duke University, shows what you can do, with just some basic social network analysis. Using metadata from Paul Revere’s Ride on the groups that people belonged to, Healy sniffs out Paul Revere as a main target. Bonus points for writing the summary from the point of a view of an 18th century analyst.

What a nice picture! The analytical engine has arranged everyone neatly, picking out clusters of individuals and also showing both peripheral individuals and—more intriguingly—people who seem to bridge various groups in ways that might perhaps be relevant to national security. Look at that person right in the middle there. Zoom in if you wish. He seems to bridge several groups in an unusual (though perhaps not unique) way. His name is Paul Revere.

You can grab the R code and dataset on github, too, if you want to follow along.

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How to: network animation with R and the iGraph

Construir animações de iformação relacional com R

Construir animações de informação relacional com R

This article lists the steps I take to create a network animation in R, provides some example source code that you can copy and modify for your own work, and starts a discussion about programming and visualization as an interpretive approach in research. Before I start, take a look at this network animation created with R and the iGraph package. This animation is of a retweet network related to #BankTransferDay. Links (displayed as lines) are retweets, nodes (displayed as points) are user accounts. For each designated period of time (in this case, an hour), retweets are drawn and then fade out over 24 hours.

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