Time-aware reciprocity prediction in trust network

Xing, Feng; Zhao, Jichang; Fang, Zhiwen; Xu, Ke

doi:10.1109/asonam.2014.6921589

Cited by 2 publications

(3 citation statements)

References 23 publications

(22 reference statements)

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“…Authors of (Cheng et al, 2011) compared structural differences of reciprocal links and parasocial links and they also studied a Twitter dataset and corresponding node features to predict reciprocal links. In another work (Feng et al, 2014), the authors reported that the majority of reciprocating links are created within a very short time after the creation of corresponding parasocial links. B. Dumba et al (Dumba et al, 2016) studied the structural properties of a reciprocal network and discussed user behavior patterns.…”

Section: Related Workmentioning

confidence: 99%

Predicting interval time for reciprocal link creation using survival analysis

Dave

Hasan

Zhang

et al. 2018

Soc. Netw. Anal. Min.

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The majority of directed social networks, such as, Twitter, Flickr, and Google+ exhibit reciprocal altruism, a social psychology phenomenon, which drives a vertex to create a reciprocal link with another vertex which has created a directed link towards the former. In existing works, scientists have already predicted the possibility of the creation of reciprocal link-a task known as "reciprocal link prediction". However, an equally important problem is determining the interval time between the creation of the first link (also called parasocial link) and its corresponding reciprocal link. No existing works have considered solving this problem, which is the focus of this paper. Predicting the reciprocal link interval time is a challenging problem for two reasons: First, there is a lack of effective features, since well-known link prediction features are designed for undirected networks and for the binary classification task, hence they do not work well for the interval time prediction; Second, the presence of ever-waiting links (i.e., parasocial links for which a reciprocal link is not formed within the observation period) makes the traditional supervised regression methods unsuitable for such data. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem to a survival analysis task and show through extensive experiments on real-

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Section: Related Workmentioning

confidence: 99%

Predicting interval time for reciprocal link creation using survival analysis

Dave

Hasan

Zhang

et al. 2018

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

show abstract

“…Instead, in [13], the reciprocity prediction problem is modeled as an outlier detection problem. Feng et al [10] show that the time delay of the reciprocity relations also has a significant impact on the reciprocity formation. Therefore, they propose a time-aware reciprocity relation prediction model by employing the reciprocity time delay.…”

Section: Reciprocity Analysis and Predictionmentioning

confidence: 99%

“…It has implications in applications that range from friend recommendation [40], bursty prediction [30], information propagation [34] to viral marketing [22]. Recent years have witnessed increasing attempts to study user reciprocal behaviors [8,10,13,32]. Nonetheless, these methods predominantly dedicated to predicting if a parasocial link will be reciprocated back in the future given a snapshot of current network topology.…”

Section: Introductionmentioning

confidence: 99%

Understanding and Predicting Delay in Reciprocal Relations

Tang

Wang

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.

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Time-aware reciprocity prediction in trust network

Cited by 2 publications

References 23 publications

Predicting interval time for reciprocal link creation using survival analysis

Predicting interval time for reciprocal link creation using survival analysis

Understanding and Predicting Delay in Reciprocal Relations

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