The structure of an online social network in most cases cannot be described just by links between its members. We study online social networks, in which members may have certain attitude, positive or negative, toward each other, and so the network consists of a mixture of both positive and negative relationships. Our goal is to predict the sign of a given relationship based on the evidences pro...

Online social networks (OSNs) recommend new friends to registered users based on local-based features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global-based ap...

Collaborative filtering is one of the most widely used techniques for recommendation system which has been successfully applied in many applications. However, it suffers from the cold start users who rate only a small fraction of the available items. In addition, these methods can not indicate confidence they are for recommendation. Trust-based recommendation methods assume the additional knowl...

Traditionally, social-network models have been descriptive rather than predictive. They’re built at a very coarse level, typically with only a few global parameters, and aren’t useful for predicting the network’s behavior. In the past, this was due largely to lack of data; the networks available for study were small and few and contained minimal information about each node. Fortunately, the Int...

Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been succesfully applied to describe cascades in a large variety of contexts. Over the last decades, many efforts have been devoted to understand the typical behaviour of the casca...