Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo

Abstract

The rapid development of online social networks (e.g., Twitter and Facebook) has promoted research related to social networks in which link prediction is a key problem. Although numerous attempts have been made for link prediction based on network structure, node attribute and so on, few of the current studies have considered the impact of information diffusion on link creation and prediction. This paper mainly addresses Sina Weibo, which is the largest microblog platform with Chinese characteristics, and proposes the hypothesis that information diffusion influences link creation and verifies the hypothesis based on real data analysis. We also detect an important feature from the information diffusion process, which is used to promote link prediction performance. Finally, the experimental results on Sina Weibo dataset have demonstrated the effectiveness of our methods.

Retweets as a Predictor of Relationships among Users on Social Media

“…We propose using two features, which we call retweet views and retweet posts . While existing research using reposting records for link prediction focuses on retweet views [24, 25], we show that retweet posts is more effective than retweet views for link prediction.…”

“…Many researchers have used an unsupervised approach for link prediction [1, 2, 7, 10–12, 25]. Unsupervised link prediction techniques estimate the likelihood of link formation (i.e., link prediction score) between two nodes by using knowledge about the characteristics of real networks.…”

“…Recent work has found that the information flow generated by user reposting is an important mechanism of link creation in social media [22, 23]. Moreover, Zhu et al [24] and Li et al [25] have used reposting records in social media for link prediction. However, research into link prediction using the records of reposting is still in its infancy, and the effectiveness of such records for link prediction has not been fully explored.…”

La Influencia de las Redes Sociales Virtuales en la Difusión de Información y Conocimiento: Estudio de PyMES

“…Dimensión OSN e ID Número Referencias de artículos analizados Sugerencia de conductas 3 (Agrawal et al, 2011), (Aragon et al, 2013, (Chelmis et al, 2013), (Chelmis et al 2014), (Crowne et al, 2015), (Garg et al, 2011), (Heimbach & Hinz, 2016), (Jiang et al, 2014), (Korzynski, 2014), (Levy et al, 2016), (Li et al, 2016), (Liang et al, 2016), (Lim et al, 2011), (Lin et al, 2012), (Lin et al, 2014), (Lipizzi et al, 2016), (Mozafari & Hamzeh, 2015), (Palacios -Marquez et al, 2016), (Roy & Zeng, 2015), (Sankar & Ravindran, 2015), (Smith, 2013), (Subires-Mancera & Olmedo Salar, 2013), (Tang et al, 2015), (Wadhwa & Bhatia, 2015), , (Wu et al, 2015), (Zaglia et al, 2015), (Zhang, 2015), (Zhao et al, 2011), (Zinoviev, 2011).…”

“…Se realiza una encuesta en empresas españolas de biotecnología y telecomunicaciones la cual confirma que la utilización de la redes para lectura, búsqueda y almacenamiento de información, y para compartir y co-crear conocimiento afecta positivamente la transferencia del conocimiento y este conocimiento ayuda a que las empresas mejoren las competencias en investigación y desarrollo. Zhang et al (2016) abordan el problema de estudiar la difusión de información entre empleados en base a contactos virtuales y tradicionales (personales, telefónicos). Proponen un modelo que indica cómo extraer canales de difusión, cómo procesar la información distribuida por los diferentes canales y cómo ponderar y seleccionar los canales.…”