2018
Flavor Network

About the work

In this project, BarabásiLab looked at the foods of regional cuisines, and developed a network-based approach to determining which combinations of ingredients taste good together. Is there a general logic behind traditions and individual tastes? Cultures have invented profusions of recipes, liked by some and rejected by others. But what determines our preference for certain flavor constituents, our liking of particular ingredient combinations?

 

Each node in the network denotes an ingredient. The node color refers to the type of food it belongs to, and its size shows the number of recipes it is used in. Two ingredients are connected when they share flavor compounds. The thicker the link, the more compounds they share. The research revealed that while Europeans prefer dishes that share flavor compounds, Asian cuisine relies on ingredients that carry complementary compounds.

Team

The Flavor Network, by Y.-Y. Ahn, S. E. Ahnert, J. P. Bagrow, and A.-L. Barabási, as published in “Flavor Network and the Principles of Food Pairing,” Scientific Reports (December 15, 2011) Layout by Nima Dehmamy, Animation by Mauro Martino

The Flavor Network as a 3-D data sculpture, by A.-L. Barabási, A. Grishchenko, N. Dehmami, and S. Milanlouei, as published in “A Structural Transition in Physical Networks,” Nature (November 28, 2018)

 

 

 

 

The lab’s first successfully rendered network sculpture was a 3-D version of the
2011 flavor network. Finalized in 2018, its play of intersecting shapes and negative space offered a whole new experience of how flavors connect. Without the color coding that so brilliantly brought it to life as a 2-D map, the flavor network’s distinct communities offer the distinction between the different food classes.

The next evolutionary stage of the BarabásiLab’s visual language is taking shape in the form of 4-D experiments, the natural dimension of networks. As Barabási and his team experiment with augmented reality and virtual reality, the mediums that could bring such networks alive, they are forging new visual expressions for their research that will surely warrant new chapters in this ever-expanding story.

 



 

ZKM_BarabásiLab. Hidden Patterns.
ZKM_BarabásiLab. Hidden Patterns.
ZKM_BarabásiLab. Hidden Patterns.
Ludwig Museum, 2020
ZKM_BarabásiLab. Hidden Patterns.
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