Predicting missing links via local feature of common neighbors

Dong, Yuxiao; Ke, Qing; Rao, Junhui; Wu, Bin

doi:10.1109/fskd.2011.6019642

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…For example, the distance-based features implicitly measured the similarities and dissimilarities of all components in the network, and the sub-graph-based features decomposed the whole network into similar groups and recognized patterns in sub-group concept. The results proved that adding more structural information and topology properties can improve link prediction performance in a social network (Dong et al , 2011; Getoor and Diehl, 2005; O’Madadhain et al , 2005).…”

Section: Discussionmentioning

confidence: 87%

E-book adoption behaviors through an online sharing platform

Wang

Chai

et al. 2019

ITP

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Purpose The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users’ product adoption behavior and to predict consumers’ purchases of user-generated products on the platform. Design/methodology/approach The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users’ purchasing behaviors. Findings The authors found that an individual’s adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers’ purchases of user-generated e-books with reasonable accuracy. Research limitations/implications The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods. Practical implications The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy. Social implications The authors verified the social influence impacting consumers’ product adoption behavior and shed light on the value of collaboration in the age of the sharing economy. Originality/value The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy.

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Section: Discussionmentioning

confidence: 87%

E-book adoption behaviors through an online sharing platform

Wang

Chai

et al. 2019

ITP

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“…Nosúltimos anos diversos trabalhos foram publicados sobre a predição de relacionamentos. De um modo geral, estes trabalhos podem ser divididos em três grupos: aqueles que utilizam apenas características da rede social (ou mais especificamente, do grafo que representa a rede social) [13,3,14,16]; aqueles que propõe a utilização de atributos (primitivos ou derivados) específicos do domínio no qual a predição irá ocorrer [35]; e sistemas híbridos que combinam estes dois aspectos [24,7,34,18].…”

Section: Trabalhos Correlatosunclassified

“…Ao se analisar os trabalhos que propõe a utilização de um ou mais atributos (ou mesmo a criação de atributos derivados)é possível observar uma gama muito grande e diversificada de atributos. Mesmo ao se restringir o domínio para predição de coautorias o número de correlatos aindaé elevado [13,7,16,18,26,28,5,4].…”

Section: Trabalhos Correlatosunclassified

“…Nas redes sociais acadêmicas, a predição de relacionamentos tem sido utilizada principalmente para a predição de coautorias [13,7,16,18,26,28], atividade que indica se um 1 A sensibilidade ou revocação corresponde a quantidade dos elementos corretamente classificados de forma positiva (verdadeiro-positivos) dividida pela quantidade total de elementos pertencentes a classe positiva. par de pesquisadores poderá/deverá colaborar na produção de um artigo.…”

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Um Sistema de Predição de Relacionamentos em Redes Sociais

Digiampietri¹,

Maruyama²,

Santiago³

et al. 2015

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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RESUMOPredizer novos relacionamentos dentro de um grupo socialé uma tarefa complexa, porém extremamenteútil para potencializar ou maximizar colaborações por meio da indicação de quais seriam as parcerias mais promissoras. Nas redes sociais acadêmicas, a predição de relacionamentosé tipicamente utilizada para tentar identificar potenciais parceiros no desenvolvimento de um projeto e/ou coautores para a publicação de um artigo. Este artigo apresenta um sistema que combina técnicas de inteligência artificial com o estado da arte das métricas de predição de relacionamentos em redes sociais. O sistema resultante foi testado usando dados reais de pesquisadores em Ciência da Computação e atingiu uma precisão superior a 99,5% na predição de novas coautorias. Palavras-Chavepredição de relacionamentos, redes sociais, redes acadêmicas ABSTRACT The prediction of new relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating what the most promising partnerships are. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents a system that combines artificial intelligence techniques with the state-ofthe-art metrics for link prediction. The resulting system was

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Community-based social recommendation under local differential privacy protection

Guo

Peng

et al. 2023

Information Sciences

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Predicting missing links via local feature of common neighbors

Cited by 5 publications

References 21 publications

E-book adoption behaviors through an online sharing platform

E-book adoption behaviors through an online sharing platform

Um Sistema de Predição de Relacionamentos em Redes Sociais

Community-based social recommendation under local differential privacy protection

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