Affiner les résultats

Type de document


Collection spécifique


Université de Fribourg

Identifying online user reputation of user–object bipartite networks

Liu, Xiao-Lu ; Liu, Jian-Guo ; Yang, Kai ; Guo, Qiang ; Han, Jing-Ti

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...

Université de Fribourg

Evolution properties of online user preference diversity

Guo, Qiang ; Ji, Lei ; Liu, Jian-Guo ; Han, Jingti

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...

Université de Fribourg

Iterative resource allocation for ranking spreaders in complex networks

Ren, Zhuo-Ming ; Zeng, An ; Chen, Duan-Bing ; Liao, Hao ; Liu, Jian-Guo

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...

Université de Fribourg

Evolution characteristics of the network core in the facebook

Liu, Jian-Guo ; Ren, Zhuo-Ming ; Guo, Qiang ; Chen, Duan-Bing

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)...

Université de Fribourg

Information filtering via weighted heat conduction algorithm

Liu, Jian-Guo ; Guo, Qiang ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2011, p. -

In this paper, by taking into account effects of the user and object correlations on a heat conduction (HC) algorithm, a weighted heat conduction (WHC) algorithm is presented. We argue that the edge weight of the user–object bipartite network should be embedded into the HC algorithm to measure the object similarity. The numerical results indicate that both the accuracy and diversity could be...

Université de Fribourg

Weighted bipartite network and personalized recommendation

Pan, Xin ; Deng, Guishi ; Liu, Jian-Guo

In: Physics Procedia, 2010, vol. 3, no. 5, p. 1867-1876

In this paper, the degree distributions of a bipartite network, namely Movielens, are investigated. The statistical analysis shows that the distribution of the degree product, ku ko, has an exponential from, where ku and ko denote the user and object degrees respectively. By introducing the edge weight effect on the recommendation performance, an improved recommendation algorithm based on mass...

Université de Fribourg

Detecting community structure in complex networks via node similarity

Pan, Ying ; Li, De-Hua ; Liu, Jian-Guo ; Liang, Jing-Zhang

In: Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, no. 14, p. 2849-2857

The detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. Based on node similarity, a fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with...

Université de Fribourg

Solving the apparent diversity-accuracy dilemma of recommender systems

Zhou, Tao ; Kuscsik, Zoltán ; Liu, Jian-Guo ; Medo, Matúš ; Wakeling, Joseph Rushton ; Zhang, Yi-Cheng

In: Proceedings of the National Academy of Sciences of the USA - PNAS, 2010, vol. 107, no. 10, p. 4511-4515

Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to...

Université de Fribourg

Degree correlation of bipartite network on personalized recommendation

Liu, Jian-Guo ; Zhou, Tao ; Zhang, Yi-Cheng ; Guo, Qiang

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...

Université de Fribourg

Improved collaborative filtering algorithm via information transformation

Liu, Jian-Guo ; Wang, Bing-Hong ; Guo, Qiang

In: International Journal of Modern Physics C, 2009, vol. 20, no. 2, p. 285-293

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to...