Evaluating Connection Quality between Two Individuals in Social Networks Along the development of social networks, predicting the quality of connections between two individuals has been attracting research interest from both industrial and research community. For many social applications with the prediction requirement, the personalities (labels) of individuals within the relationship (connections) were known to have a significant impact against the analysis, observing two neighbored individuals acquiring an identical label are more likely to affect each other than those are only with different labels. In the paper, we tackle with a more practical problem in which an individual has several labels, rather than considering only an identical label for all individuals. For the task, we first present multilabel independent cascade (MIC) model which is in fact a generalization of the classical IC model, and then in a multilabel social network, we propose two algorithms for analyzing the maximum connectivity between two individuals. The first is a heuristic algorithm, which, generalizing the maximal flow algorithm, is based on repeatedly finding shortest augmenting path; the second is based on linearprogramming (LP) and produces optimum solutions. At last, we evaluate the two algorithms by experiments through their practical performance and runtime.

Link Classification and Tie Strength Ranking in Online Social Networks with Exogenous Interaction Networks 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 todayxe2x80x99s 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 nonnoisy links in a network. In this paper (The work in this paper is based on and is an extension of our previous work 2.), 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. The reasons behind that as well as our recommendation about which classifiers to use are presented.