University of California, San Diego (UCSD) researchers recently published a paper exploring the concept of popularity versus similarity and how it impacts the growth of networks.
The study shows how networks evolve optimizing a trade-off between popularity and similarity. The researchers found that although popularity attracts new connections, similarity is just as attractive.
"Popular nodes in a network, or those that are more connected than others, tend to attract more new connections in growing networks, but similarity between nodes is just as important because it is instrumental in determining precisely how these networks grow," says UCSD's Dmitri Krioukov.
The researchers developed a model that significantly increases the accuracy of network evolution prediction by considering the trade-offs between popularity and similarity. The model describes the large-scale evolution of technological networks, social networks, and biological networks. The researchers note that model's ability to predict links in networks could be used to predict protein interactions or terrorist connections or to improve recommender and collaborative-filtering systems. "If we know the laws describing the dynamics of a complex system, then we not only can predict its behavior, but we may also find ways to better control it," Krioukov says.
From UC Newsroom
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