Mining Missing Hyperlinks from Human Navigation Traces

West, Robert; Paranjape, Ashwin; Leskovec, Jure

doi:10.1145/2736277.2741666

Cited by 51 publications

(29 citation statements)

References 46 publications

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“…Insights have been utilized to infer missing links [44], to predict break-ups of the navigation process [36], for recommendations [42], or to improve the link structure of a website [32]. For the latter, Paranjape et al [32] highlighted the importance of improving hyperlink structures based on their usage due to the large amount of unused links.…”

Section: Related Workmentioning

confidence: 99%

What Makes a Link Successful on Wikipedia?

Dimitrov

Singer

Lemmerich

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures

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Section: Related Workmentioning

confidence: 99%

What Makes a Link Successful on Wikipedia?

Dimitrov

Singer

Lemmerich

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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“…One of the first models in this domain is the famous random surfer [26]. Detailed user studies have been performed on, for example, navigation behavior of Wikipedia [33,34] and the relationship between queries and subsequent web navigation [8,14].…”

Section: Related Workmentioning

confidence: 99%

Modeling User Consumption Sequences

Benson

Kumar

Tomkins

2016

Proceedings of the 25th International Conference on World Wide Web

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We study sequences of consumption in which the same item may be consumed multiple times. We identify two macroscopic behavior patterns of repeated consumptions. First, in a given user's lifetime, very few items live for a long time. Second, the last consumptions of an item exhibit growing inter-arrival gaps consistent with the notion of increasing boredom leading up to eventual abandonment.We then present what is to our knowledge the first holistic model of sequential repeated consumption, covering all observed aspects of this behavior. Our simple and purely combinatorial model includes no planted notion of lifetime distributions or user boredom; nonetheless, the model correctly predicts both of these phenomena. Further, we provide theoretical analysis of the behavior of the model confirming these phenomena. Additionally, the model quantitatively matches a number of microscopic phenomena across a broad range of datasets.Intriguingly, these findings suggest that the observation in a variety of domains of increasing user boredom leading to abandonment may be explained simply by probabilistic conditioning on an extinction event in a simple model, without resort to explanations based on complex human dynamics.

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“…Применение в [23] полученных результатов к анализу эмпирических данных веб-сканирования показало, что в растущей сети веб-страниц непрерывно теряется около 15% связей (ссылок). В статье [25] разрабатываются методы восстановления потерянных связей в реальных сетях. В [26,27] исследуются графы БА с потерями вершин и ребер.…”

Section: Ck Q K~unclassified

Growing Graphs With Losing of Arcs

Zadorozhnyi¹,

Yudin²,

Yudina³

2018

AM&FI

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Разрабатываются методы расчета характеристик случайных графов, используемых для моделирования растущих сетей. Для случайных графов предпочтительного связывания, растущих с непрерывной потерей части дуг, решена задача их калибровки по распределению степеней связности вершин и задача расчета двумерного распределения степеней связности дуг (ребер). Применение разработанных методов расчета характеристик графов с потерями дуг позволяет синтезировать адекватные модели растущих сетей (социальных, информационно-телекоммуникационных, сетей сотрудничества и т.д.) для разработки эффективных стратегий взаимодействия с исследуемыми сетями и управления их развитием. Ключевые слова: случайные графы с нелинейным правилом предпочтительного связывания, распределение степеней связности вершин, распределение степеней связности дуг, калибровка случайных графов.

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Mining Missing Hyperlinks from Human Navigation Traces

Cited by 51 publications

References 46 publications

What Makes a Link Successful on Wikipedia?

What Makes a Link Successful on Wikipedia?

Modeling User Consumption Sequences

Growing Graphs With Losing of Arcs

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