An evolving model of online bipartite networks

Zhang, Chuxu; Zhang, Zi-Ke; Liu, Chuang

doi:10.1016/j.physa.2013.07.027

Cited by 22 publications

(22 citation statements)

References 66 publications

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“…To eliminate the data sparsity effect, in both datasets, we purify the data to guarantee that [47] (a) each user has collected at least one object; (b) each object has been collected by at least two users, and assigned by at least two tags; (c) each tag is used by at least two users. Table 1 shows the basic statistics of the observed data sets.…”

Section: Gravity Based Recommender Systemsmentioning

confidence: 99%

Gravity Effects on Information Filtering and Network Evolving

et al. 2014

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In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.

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Section: Gravity Based Recommender Systemsmentioning

confidence: 99%

Gravity Effects on Information Filtering and Network Evolving

et al. 2014

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“…In this regard, the adaptation of evolving models of bipartite networks to the tourism context is an useful way to represent the phenomenon. Evolving models has been theorized for one-mode networks [22,23] and extensions to several cases of bipartite networks have been also proposed [24][25][26][27][28][29][30]. Following the same basis of one-mode evolving networks, these models assume that new nodes of both categories appear in every time step.…”

Section: Introductionmentioning

confidence: 99%

An evolving model for the lodging-service network in a tourism destination

Hernández

González-Martel

2017

Physica A: Statistical Mechanics and its Applications

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Tourism is a complex dynamic system including multiple actors which are related each other composing an evolving social network. This paper presents a growing bipartite network model that explains the rise of the supply network in a tourism destination from the beginning phases of development. The nodes are the lodgings and services in a destination and a link between them appears if a representative tourist hosted in the lodging visits/consumes the service during his/her stay.The specific link between both categories are determined by a random and preferential attachment rule. The analytic results show that the long-term degree distribution of services follows a shifted power-law distribution. The numerical simulations show slight disagreements with the theoretical results in the case of the one-mode degree distribution of services, due to the low order of convergence to zero of X-motifs. The model predictions are compared with real data coming from a popular tourist destination in Gran Canaria, Spain, showing a good agreement between numerical and empirical data.

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“…Zhang et al [15] proposed an evolutionary hypergraph model to reconstruct the online user-tag-resource network, showing the user preferences to save resources with tags they are interested in. An evolving network model then was introduced to reproduce the user selection patterns on tags, music and movies [16]. Thanks to the development of web 2.0, online users could not only select, browse, but also review and share what they like.…”

Section: Introductionmentioning

confidence: 99%

Evolution properties of online user preference diversity

Guo

Liu

et al. 2017

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• The evolution pattern of the online user preference is investigated.• The user rating preference would become diverse and then get centralized finally.• The correlation between the user ratings and the object qualities keeps increasing.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 that, for movies and reviews, the user rating preference would become diverse and then get centralized finally. By introducing the empirical variation coefficient, we present a Markov model, which could regenerate the evolution properties of two online systems regarding to the stable variation coefficients. In addition, we investigate the evolution of the correlation between the user ratings and the object qualities, and find that the correlation would keep increasing as the user degree increases. This work could be helpful for understanding the anchoring bias and memory effects of the online user collective behaviors.

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An evolving model of online bipartite networks

Cited by 22 publications

References 66 publications

Gravity Effects on Information Filtering and Network Evolving

Gravity Effects on Information Filtering and Network Evolving

An evolving model for the lodging-service network in a tourism destination

Evolution properties of online user preference diversity

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