Social networks contain implicit knowledge that can be used to infer hierarchical relations that are not explicitly present in the available data. Interaction patterns are typically affected by use ...
An extensive amount of research has been performed on inferring the type and the strength of social ties in a network. Sintos and Tsaparas @cite_12 examine the problem of labeling connections in a network depending on whether they are strong or weak using only the graph structure of the network. Similarly, Xiang and colleagues @cite_3 estimate relationships' strength by considering interaction activity and user similarity based on the Homophily theory in order to develop an unsupervised model that exhibits the latent properties of the network. @cite_0 utilize the statistical properties of communication patterns among actors to deduce the type and strength of links in a network. Backstrom and Kleinberg @cite_4 use the underlying network structure to identify the most influential person in an actor's social network neighborhood. @cite_8 focus on the problem of labeling the edges in a social network as positive or negative based on the user behavior of decision-making.
@cite_4 @cite_8 @cite_3 @cite_0 @cite_12
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We study the problem of labeling the edges of a social network graph (e.g., acquaintance connections in Facebook) as either positive (i.e., trust, true friendship) or negative (i.e., distrust, possible frenemy) relations. Such signed relations provide much stronger signal in tying the behavior of online users than the unipolar Homophily effect, yet are largely unavailable as most social graphs only contain unsigned edges. We show the surprising fact that it is possible to infer signed social ties with good accuracy solely based on users' behavior of decision making (or using only a small fraction of supervision information) via unsupervised and semi-supervised algorithms. This work hereby makes it possible to turn an unsigned acquaintance network (e.g. Facebook, Myspace) into a signed trust-distrust network (e.g. Epinion, Slashdot). Our results are based on a mixed effects framework that simultaneously captures users' behavior, social interactions as well as the interplay between the two. The framework includes a series of latent factor models and it also encodes the principles of balance and status from Social psychology. Experiments on Epinion and Yahoo! Pulse networks illustrate that (1) signed social ties can be predicted with high-accuracy even in fully unsupervised settings, and (2) the predicted signed ties are significantly more useful for social behavior prediction than simple Homophily. This paper examines the celebrated "Strength of weak ties" theory of Granovetter(1973). We formalize the theory in terms of two hypotheses: one, for any threeplayers with two links present, the probability of a third link being present isincreasing in the strength of the two ties, and two, the removal of a weak tieincreases average distance in the network more than the removal of a strong tie. We test these hypotheses using data on the network of coauthorship amongeconomists. We find support for the hypothesis of transitivity of strong ties, but we reject thehypothesis that weak ties reduce distance more than strong ties do. We then identify two general features of networks which explain these findings:significant inequality in the distribution of connections across individuals andstronger ties among individuals who have more connections. The widespread use of online social networks, such as Facebook and Twitter, is generating a growing amount of accessible data concerning social relationships. The aim of this work is twofold. First, we present a detailed analysis of a real Facebook data set aimed at characterising the properties of human social relationships in online environments. We find that certain properties of online social networks appear to be similar to those found “offline” (i.e., on human social networks maintained without the use of social networking sites). Our experimental results indicate that on Facebook there is a limited number of social relationships an individual can actively maintain and this number is close to the well-known Dunbar’s number (150) found in offline social networks. Second, we also present a number of linear models that predict tie strength (the key figure to quantitatively represent the importance of social relationships) from a reduced set of observable Facebook variables. Specifically, we are able to predict with good accuracy (i.e., higher than 80 ) the strength of social ties by exploiting only four variables describing different aspects of users interaction on Facebook. We find that the recency of contact between individuals – used in other studies as the unique estimator of tie strength – has the highest relevance in the prediction of tie strength. Nevertheless, using it in combination with other observable quantities, such as indices about the social similarity between people, can lead to more accurate predictions This article presents a network analysis of word-of-mouth referral behavior in a natural environment. The relational properties of tie strength and homophily were employed to examine referral behavior at micro and macro levels of inquiry. The study demonstrates different roles played by weak and strong social ties. At the macro level, weak ties displayed an important bridging function, allowing information to travel from one distinct subgroup of referral actors to another subgroup in the broader social system. At the micro level, strong and homophilous ties were more likely to be activated for the flow of referral information. Strong ties were also perceived as more influential than weak ties, and they were more likely to be utilized as sources of information for related goods.