The prediction of graph evolution is an important and challenging problem in the analysis of networks and of the Web in particular. But while the appearance of new links is part of virtually every model of Web growth, the disappearance of links has received much less attention in the literature. To fill this gap, our approach DecLiNe (an acronym for DECay of LInks in NEtworks) aims to predict link decay in networks, based on structural analysis of corresponding graph models. In analogy to the link prediction problem, we show that analysis of graph structures can help to identify indicators for superfluous links under consideration of common network models. In doing so, we introduce novel metrics that denote the likelihood of certain links in social graphs to remain in the network, and combine them with state-of-the-art machine learning methods for predicting link decay. Our methods are independent of the underlying network type, and can be applied to such diverse networks as the Web, social networks and any other structure representable as a network, and can be easily combined with case-specific content analysis and adopted for a variety of social network mining, filtering and recommendation applications. In systematic evaluations with large-scale datasets of Wikipedia we show the practical feasibility of the proposed structure-based link decay prediction algorithms.
For social networks, most studies focus on non-structural reasons for the disappearance of links, such as interactions between people. Examples are the removal of friendships on Facebook ( unfriending'') @cite_4 , and the removal of follow links on Twitter ( unfollowing'') @cite_14 @cite_9 . A recent study @cite_11 finds that the most common reason for unfriending on Facebook is over political opinions. In all these works, the only structural indicators used for predicting the disappearance of social links are the number of common neighbors in @cite_8 , @cite_9 and @cite_4 . All three studies find that links connecting nodes with many common neighbors are less likely to be deleted from the social graph, and that content and interaction features are more predictive for link disappearance in social networks. Since the form of content and in particular interaction is fundamentally different among people than hyperlinks between pages, these methods cannot be generalized to predict the disappearance of hyperlinks.
@cite_14 @cite_4 @cite_8 @cite_9 @cite_11
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We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results. They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter. Recent analyses of self-reported data (mainly survey data) seem to suggest that social rules for ending relationships are transformed on Facebook. There seem to be a radical difference between offline and online worlds: reasons for ending online relationships are different than those for ending offline ones. These preliminary findings are, however, not supported by any quantitative evidence, and that is why we put them to test. We consider a variety of factors (e.g., age, gender, personality traits) that studies in sociology have found to be associated with friendship dissolution in the real world and study whether these factors are still important in the context of Facebook. Upon analyzing 34,012 Facebook relationships, we found that, on average, a relationship is more likely to break if it is not embedded in the same social circle, if it is between two people whose ages differ, and if one of the two is neurotic or introvert. Interestingly, we also found that a relationship with a common female friend is more robust than that with a common male friend. These findings are in line with previous analyses of another popular social-networking platform, that of Twitter. All this goes to suggest that there is not much difference between offline and online worlds and, given this predictability, one could easily build tools for monitoring online relations. We investigate the breaking of ties between individuals in the online social network of Twitter, a hugely popular social media service. Building on sociology concepts such as strength of ties, embeddedness, and status, we explore how network structure alone influences tie breaks - the common phenomena of an individual ceasing to "follow" another in Twitter's directed social network. We examine these relationships using a dataset of 245,586 Twitter "follow" edges, and the persistence of these edges after nine months. We show that structural properties of individuals and dyads at Time 1 have a significant effect on the existence of edges at Time 2, and connect these findings to the social theories that motivated the study. We propose a logistic regression model taking into account two analytically different sets of factors–structure and action. The factors include individual, dyadic, and triadic properties between ego and alter whose tie breakup is under consideration. From the fitted model using a large-scale data, we discover 5 structural and 7 actional variables to have significant explanatory power for unfollow. One unique finding from our quantitative analysis is that people appreciate receiving acknowledgements from others even in virtually unilateral communication relationships and are less likely to unfollow them: people are more of a receiver than a giver.