In: Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, p. 188–199
Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the...
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In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 391, no. 4, p. 1777–1787
Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational...
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In: Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, no. 23, p. 4867-4871
In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain...
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