  Online social networks (OSNs) have become the main medium for connecting
people, sharing knowledge and information, and for communication. The social
connections between people using these OSNs are formed as virtual links (e.g.,
friendship and following connections) that connect people. These links are the
heart of today's OSNs as they facilitate all of the activities that the members
of a social network can do. However, many of these networks suffer from noisy
links, i.e., links that do not reflect a real relationship or links that have a
low intensity, that change the structure of the network and prevent accurate
analysis of these networks. Hence, a process for assessing and ranking the
links in a social network is crucial in order to sustain a healthy and real
network. Here, we define link assessment as the process of identifying noisy
and non-noisy links in a network. In this paper, we address the problem of link
assessment and link ranking in social networks using external interaction
networks. In addition to a friendship social network, additional exogenous
interaction networks are utilized to make the assessment process more
meaningful. We employed machine learning classifiers for assessing and ranking
the links in the social network of interest using the data from exogenous
interaction networks. The method was tested with two different datasets, each
containing the social network of interest, with the ground truth, along with
the exogenous interaction networks. The results show that it is possible to
effectively assess the links of a social network using only the structure of a
single network of the exogenous interaction networks, and also using the
structure of the whole set of exogenous interaction networks. The experiments
showed that some classifiers do better than others regarding both link
classification and link ranking.
