In: Journal of Informetrics, 2020, vol. 14, no. 1, p. 101005
Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 network-based metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation...
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In: Information Sciences, 2019, vol. 488, p. 257–271
When users search online for content, they are constantly exposed to rankings. For example, web search results are presented as a ranking of relevant websites, and online bookstores often show us lists of best-selling books. While popularity-based ranking algorithms (like Google’s PageRank) have been extensively studied in previous works, we still lack a clear understanding of their...
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In: Entropy, 2018, vol. 20, no. 10, p. 777
Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for...
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In: The European Physical Journal B, 2009, vol. 71, no. 4, p. 565-571
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In: The European Physical Journal B, 2008, vol. 66, no. 4, p. 557-561
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In: The European Physical Journal B, 2009, vol. 71, no. 4, p. 623-630
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In: Proceedings of the National Academy of Sciences, 2015, vol. 112, no. 8, p. 2325–2330
The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in...
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In: New Journal of Physics, 2014, vol. 16, no. 6, p. 063057
Recommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects' (and/or users') similarity rather than on their difference. Such approaches are subject to a high risk of increasingly exposing users to a narrowing band of popular objects. As a result, a few objects may be...
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In: Physica A: Statistical Mechanics and its Applications, 2014, vol. 404, p. 47–55
Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank (Lü et al., 2011). According to the simulations on the standard SIR model, the weighted LeaderRank performs better than...
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In: PLoS ONE, 2013, vol. 8, no. 10, p. e77455
Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to...
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