link prediction problem. Intuitively, a friendship link between two users can be predicted based on their common friends and interests. However, using user interests directly can be challenging, given the large number of possible interests. In the past, approaches that make use of an explicit user

: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties

there is an option for making profile attributes private, if links and group affiliations are known, users ’ privacy in social networks may be compromised. On a dataset from a well-known social-media website, we could easily recover the sensitive attributes for half of the private-profile users with a high accuracy

constructed based on the interest ontology. Previous work has shown that the accuracy of predicting friendship links in this network is very low if simply interests common to two users are used as features and no network graph features are considered. Thus, our goal is to organize users ’ interests

In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event

Numerous studies show that happy individuals are successful across multiple life domains, including marriage, friendship, income, work performance, and health. The authors suggest a conceptual model to account for these findings, arguing that the happiness–success link exists not only because

studies have documented the link between peer rejection and childhood externalizing problems, such as antisocial behavior (for reviews, see Boivin, Vitaro, & Poulin, 2005; Rubin et al., 1998). Some studies have also shown that peer rejection is associ-atedwithheightened risk for internalizingproblems

Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that is crucial

broad group of acquaintances, the analysis shows that friendship links involving this attractive agent are most likely to form.

ABSTRACT We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets