Nature 524, 38–39 (06 August 2015)


In complex networks, some nodes are more important than others. The most important nodes are those whose elimination induces the network’s collapse, and identifying them is crucial in many circumstances, for example, if searching for the most effective way to stop a disease from spreading. But this is a hard task, and most methods available for the purpose are essentially based on trial-and-error. Here, Flaviano Morone and Hernán Makse devise a rigorous method to determine the most influential nodes in random networks by mapping the problem onto optimal percolation and solving the optimization problem with an algorithm that the authors call ‘collective influence’. They find that the number of optimal influencers is much smaller, and that low-degree nodes can play a much more important role in the network than previously thought.

See paper Flaviano Morone and Hernán Makse, “Influence maximization in complex networks through optimal percolation”, Nature 524, 65-68 (2015) and web interface of the calculation.

The retweet data in Twitter that we used in this paper can be downloaded here.


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