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: Journal of Statistical Mechanics: Theory and Experiment, 2015, vol. 2015, no. 7, p. P07020
Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this...
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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: PLoS ONE, 2014, vol. 9, no. 10, p. e111005
Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs...
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In: PLoS ONE, 2014, vol. 9, no. 5, p. e97146
How to design an accurate and robust ranking algorithm is a fundamental problem with wide applications in many real systems. It is especially significant in online rating systems due to the existence of some spammers. In the literature, many well-performed iterative ranking methods have been proposed. These methods can effectively recognize the unreliable users and reduce their weight in judging...
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In: PLoS ONE, 2014, vol. 9, no. 5, p. e96614
Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their...
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In: Physica A: Statistical Mechanics and its Applications, 2014, vol. 406, p. 139–144
In social sciences, there is currently rare consensus on the underlying mechanism for cultural evolution, partially due to lack of suitable data. The evolution of first names of newborn babies offers a remarkable example for such researches. In this paper, we employ the historical data on baby names from the United States to investigate the evolutionary process of culture, in particular focusing...
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In: EPL (Europhysics Letters), 2014, vol. 105, no. 5, p. 58002
Recommender systems provide a promising way to address the information overload problem which is common in online systems. Based on past user preferences, a recommender system can find items that are likely to be relevant to a given user. Two classical physical processes, mass diffusion and heat conduction, have been used to design recommendation algorithms and a hybrid process based on them has...
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In: Physica A: Statistical Mechanics and its Applications, 2014, vol. 400, p. 47–56
We develop a probabilistic consumer choice framework based on information asymmetry between consumers and firms. This framework makes it possible to study market competition of several firms by both quality and price of their products. We find Nash market equilibria and other optimal strategies in various situations ranging from competition of two identical firms to firms of different sizes and...
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In: PLoS ONE, 2013, vol. 8, no. 11, p. e79354
With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot...
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