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Université de Fribourg

Recommender systems

Lü, Linyuan ; Medo, Matúš ; Yeung, Chi Ho ; Zhang, Yi-Cheng ; Zhang, Zi-Ke ; Zhou, Tao

In: Physics Reports, 2012, vol. 519, no. 1, p. 1–49

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification...

Université de Fribourg

Enhancing topology adaptation in information-sharing social networks

Cimini, Giulio ; Chen, Duanbing ; Medo, Matúš ; Lü, Linyuan ; Zhang, Yi-Cheng ; Zhou, Tao

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2012, vol. 85, no. 4, p. 046108

The advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an...

Université de Fribourg

How altruism works: An evolutionary model of supply networks

Ge, Zehui ; Zhang, Zi-Ke ; Lü, Linyuan ; Zhou, Tao ; Xi, Ning

In: Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, no. 3, p. 647–655

Recently, supply networks have attracted increasing attention from the scientific community. However, it lacks serious consideration of social preference in Supply Chain Management. In this paper, we develop an evolutionary decision-making model to characterize the effects of suppliers’ altruism in supply networks, and find that the performances of both suppliers and supply chains are improved...

Université de Fribourg

small world yields the most effective information spreading

Lü, Linyuan ; Chen, Duan-Bing ; Zhou, Tao

In: New Journal of Physics, 2011, vol. 13, p. 123005

The spreading dynamics of information and diseases are usually analyzed by using a unified framework and analogous models. In this paper, we propose a model to emphasize the essential difference between information spreading and epidemic spreading, where the memory effects, the social reinforcement and the non-redundancy of contacts are taken into account. Under certain conditions, the...

Université de Fribourg

Tag-aware recommender systems: a state-of-the-art survey

Zhang, Zi-Ke ; Zhou, Tao ; Zhang, Yi-Cheng

In: Journal of Computer Science and Technology, 2011, vol. 26, no. 5, p. 767-777

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically,...

Université de Fribourg

Link prediction in complex networks: a local naïve Bayes model

Liu, Zhen ; Zhang, Qian-Ming ; Lü, Linyuan ; Zhou, Tao

In: Europhysics Letters, 2011, vol. 96, no. 4, p. 48007

The common-neighbor–based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In the traditional method, each common neighbor of two nodes contributes equally to the connection likelihood. In this letter, we argue that different common neighbors may play different roles and thus contributes...

Université de Fribourg

Identifying influential nodes in complex networks

Lü, Linyuan ; Shang, Ming-Sheng ; Zhang, Yi-Cheng ; Zhou, Tao

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...

Université de Fribourg

A robust ranking algorithm to spamming

Zhou, Yan-Bo ; Lei, Ting ; Zhou, Tao

In: EPL - Europhysics Letters, 2011, vol. 94, no. 4, p. 48002

Ranking problem of web-based rating systems has attracted much attention. A good ranking algorithm should be robust against spammer attack. Here we proposed a correlation-based reputation algorithm to solve the ranking problem of such rating systems where user votes some objects with ratings. In this algorithm, the reputation of a user is iteratively determined by the correlation coefficient...

Université de Fribourg

Onset of cooperation between layered networks

Gu, Chang-Gui ; Zou, Sheng-Rong ; Xu, Xiu-Lian ; Qu, Yan-Qing ; Jiang, Yu-Mei ; He, Da Ren ; Liu, Hong-Kun ; Zhou, Tao

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2011, vol. 84, no. 2, p. 026101

Functionalities of a variety of complex systems involve cooperations among multiple components; for example, a transportation system provides convenient transfers among airlines, railways, roads, and shipping lines. A layered model with interacting networks can facilitate the description and analysis of such systems. In this paper we propose a model of traffic dynamics and reveal a transition at...

Université de Fribourg

Leaders in social networks, the delicious case

Lü, Linyuan ; Zhang, Yi-Cheng ; Yeung, Chi Ho ; Zhou, Tao

In: PLoS ONE, 2011, vol. 6, no. 6, p. e21202

Finding pertinent information is not limited to search engines. Online communities can amplify the influence of a small number of power users for the benefit of all other users. Users' information foraging in depth and breadth can be greatly enhanced by choosing suitable leaders. For instance in delicious.com, users subscribe to leaders' collection which lead to a deeper and wider reach not...