In: Physica A: Statistical Mechanics and its Applications, 2018, vol. 499, p. 490–497
Understanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki users’ online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the...
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In: International Journal of Modern Physics C, 2010, vol. 21, no. 1, p. 137-147
In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is...
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In: International Journal of Modern Physics C, 2009, vol. 20, no. 12, p. 1925-1932
In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their...
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In: PLoS ONE, 2014, vol. 9, no. 8, p. e104028
Statistical properties of the static networks have been extensively studied. However, online social networks are evolving dynamically, understanding the evolving characteristics of the core is one of major concerns in online social networks. In this paper, we empirically investigate the evolving characteristics of the Facebook core. Firstly, we separate the Facebook-link(FL) and Facebook-wall(FW)...
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In: Physica A: Statistical Mechanics and its Applications, 2017, vol. 468, p. 698–713
Detecting the evolution properties of online user preference diversity is of significance for deeply understanding online collective behaviors. In this paper, we empirically explore the evolution patterns of online user rating preference, where the preference diversity is measured by the variation coefficient of the user rating sequence. The statistical results for four real systems show...
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In: Physics Letters A, 2017, vol. 381, no. 11, p. 970–975
The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities...
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In: The European Physical Journal B - Condensed Matter and Complex Systems, 2012, vol. 85, no. 8, p. 286
Information filtering based on structure properties of user-object bipartite networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate the framework of heat-conduction-based (HC) information filtering [Y.-C. Zhang et al., Phys. Rev. Lett. 99, 154301 (2007)] in terms of the local node similarity. We compare nine well-known...
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In: Physica A: Statistical Mechanics and its Applications, 2018, vol. 494, p. 403–409
Identifying online user reputation is significant for online social systems. In this paper, taking into account the preference physics of online user collective behaviors, we present an improved group-based rating method for ranking online user reputation based on the user preference (PGR). All the ratings given by each specific user are mapped to the same rating criteria. By grouping users...
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In: Physica A: Statistical Mechanics and its Applications, 2017, vol. 467, p. 508–516
Identifying online user reputation based on the rating information of the user–object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part...
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In: Frontiers of Computer Science in China, 2009, vol. 3, no. 3, p. 417-420
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