In: Physica A: Statistical Mechanics and its Applications, 2018, vol. 508, p. 213–222
Understanding the social relation of dynamical online social networks (OSNs) is significant for identifying the strong and weak ties. In this paper, we empirically investigate the evolution characteristics of Facebook and Wiki users’ social signature, capturing the distribution of frequency of interactions between different alters over time in ego network. The statistical results show that...
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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: The European Physical Journal B, 2008, vol. 66, no. 4, p. 557-561
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In: Physica A: Statistical Mechanics and its Applications, 2018, vol. 494, p. 422–429
Understanding the popularity dynamics of online application(App) is significant for the online social systems. In this paper, by dividing the Facebook Apps into different groups in terms of their popularities, we empirically investigate the popularity dynamics for different kinds of Facebook Apps. Then, taking into account the influence of cumulative and recent popularities on the user...
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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: EPL (Europhysics Letters), 2017, vol. 118, no. 4, p. 48001
It is a challenging work to assess research performance of multiple institutes. Considering that it is unfair to average the credit to the institutes which is in the different order from a paper, in this paper, we present a credit allocation method (CAM) with a weighted order coefficient for multiple institutes. The results for the APS dataset with 18987 institutes show that top-ranked...
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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: 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: 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: EPL (Europhysics Letters), 2014, vol. 106, no. 4, p. 48005
Ranking the spreading influence of nodes in networks is a very important issue with wide applications in many different fields. Various topology-based centrality measures have been proposed to identify influential spreaders. However, the spreading influence of a node is usually not only determined by its own centrality but also largely influenced by the centrality of neighbors. To incorporate the...
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