The Secret Ingredient of the World’s Best Apple Pie

Algorythms can predict the future, but unlike the Delphi oracle, they do it based on hard scientific methods, instead of intestines and animal bones. Finding old friends on Facebook, recomending books you might like on Amazon, or predicting the outcome of the 2012 presidential election – you name it, an algorythm does it.

So what about food? Could these same math whizzes help us bake a better pumpkin pie or mix up a tastier batch of sweet potatoes this Christmas? Lada Adamic, a computer scientist at the University of Michigan and Facebook, thinks it just might be possible. she and her team have come up with an algorithm to guess how successful a recipe will turn out. And the math works surprisingly well. It predicts with nearly 80 percent accuracy how many stars your mother's cranberry recipe will receive on allrecipes.com. Plus, it can recommend ingredient replacements to make your pie crust and potatoes more healthful.

She and her team took nearly 50,000 recipes and 2 million reviews from allrecipes.com and then hacked up an algorithm to extract out all the ingredients, cooking methods and nutritional profiles. With just these items, her algorithm could predict the recipe's rating with an accuracy of about 70 percent. But the magic happened when Adamic built a "social network" for the ingredients. She looked at how often two ingredients appear in the same recipes. Those that frequently show up together — milk and butter, nutmeg and cinnamon, basil and rosemary — sit close to each other in the network, but those that rarely appear in the same dish, such as coconut and parsley, are far from each other.



Physicists at Harvard University performed a similar network analysis on ingredients' flavors, but Adamic took it a step further and integrated the data into a recipe prediction program.

Adamic's network analysis boosted the accuracy of her recipe recommendations by about 10 percent. But it also revealed a treasure-trove of information about the way Americans mix and match ingredients, which ones we like to leave out or throw in extra.
Her algorithm analyzed reviewers' recommendations for customizing recipes, such as "I replaced the butter in the frosting by sour cream, just to soothe my conscience about all the fatty calories" and "This is a great recipe, but using fresh tomatoes only adds a few minutes to the prep time." Then the mathematics stitched together little clusters or communities of interchangeable foods and spices.

The result is a list of recipe replacements more comprehensive and scientifically accurate than anything you'll find in the Joy of Cooking or online.

Read the full article on arXiv.com, or a lighter verison on the npr blog.

Network Science of the Game of Go

You can make networks from pretty much anything. Connect music based on taste or phone calls, companies based on their ownership, spread routes of abstract movements, and lots more. It is high time to start using networks to understand games. But what of the structure of games themselves? In a paper that was recently published in EurophysicsLetters, two French scientists decided to apply network science to the game of Go.

They constructed their networks in a simple way: If one board position can lead to another, they are connected. Using a dataset of about 1,000 professional games and 4,000 amateur games, they began to construct these networks.

In a Game of Go players put black and white stones on a grid board.

Of course, the Go board is very large and so you can’t compare entire board layouts. Instead, they decided to make it much more tractable and look at the board composition surrounding a newly placed piece (a move in Go consists of putting a stone on an intersection of the grid lines of the board). In this case, they looked at the pieces immediately surrounding a newly placed piece (for a 3×3 grid). They calculated that this creates 1107 possible moves, which can be connected if the moves occur one after another, and are in the same region of the board. They also examined the frequency of moves, which obeys a heavy-tailed distribution (whether or not it is a power-law as they claim seems a bit weaker).

The network analyses in the paper are a bit odd, though they find many classic graph structures, such as a heavy-tailed link distribution and high amounts of clustering. Gratifyingly, the networks constructed from amateur and professional games are distinct, though in somewhat subtle ways. 

Read the article here, or the short version on Wired!

SNA – The Secret Weapon against Terrorism

In his forthcoming book Network Science, Albert-László Barabási has already reported about the role of social network analysis in the capturing on Saddam Hussein. Our readers know, that the blog itself is no stranger to the subject. A new American paper sums up how and why this approach can be useful in fighting political violence.

The academic community studying terrorism has changed dramatically in the past decade, and the descriptive and explanatory potentials have grown strongly. On of the reasons for its popularity is the increasing acknowledgment within the academic community of the important association between the group’s dynamic and (social)structure, and its members’ motivations and behaviors.

The Network of the Terrorist Group responsible for the Attack against WTC.

Understanding the motives and the processes that led the group to engage in political violence requires a look beyond the apparent causal relations between the causes of the violence and the violent activities. Since September 11th, growing numbers of media outlets have increased their coverage of terrorist incidents and groups. This, combined with the striking increase in the efforts and resources invested in data collection about these groups by academic and governmental agencies in recent years.

An IRA statement.

Violence or political action is a result of collective action, i.e., an output of a process, which is an action of a group of actors who interact with each other on some level, so SNA seems like an obvious analyzing tool. The sizes of these gropus varies widely (from 2 man groups to milites like the IRA), so an instrumental approach in bigger newtorks focus on command and information channels and the roles of leaders.

After drawing the networks of terrorist organizations, measuring the influence and power of individual actors becomes relatively easy, with the help of centrality and betweenness measures. Unveiling the hierarchy could also help authorities in dismantling them, making targeting a lot less complicated.

You can read the whole article at the academia homepage.

Ckeck out the original article for more.

Research is the New Discovery

A software called Livaplasma helps you discover new music you might like, with music you already do. 

This is what a music search looks like, if you look for music like Led Zeppelin, bands from the are and the genre become part of the network. Cream, The Who and Pink Floyd are the top recommendations.

The crator of the site Frédéric Vavrille, made the site back in 2004. But music recommendations is not all Liveplasma can do. It also works for books and movies.

With Inception as a key word, the network consist of other Cristopher Nolan movies, and genre specific works like The Girl With the Dragon Tattoo and The Matrix.

Jane Eyre's networks include Jane Austen's other works, books by Dickens and the Bronte sisters.

Curious? Check out the site for yourself at liveplasma.com !