Relational intelligence recognition in online social networks xe2x80x94 A survey Abstract   Information networks today play an important, fundamental role in regulating real life activities. However, many methods developed on this framework lack the capacity to adequately represent sophistication contained within the information it carries. As a result, they suffer from problems such as inaccuracies, reliability and performance. We define relational intelligence as a combination of affective (Cambria, 2016; 2015  1 ,  2 ; Hidalgo etxc2xa0al., 2015  3 ), sentimental (Ferrara and Yang, 2015  4 ; Wang etxc2xa0al., 2013  5 ; Madhoushi etxc2xa0al., 2015  6 ) and ethical (Vayena etxc2xa0al., 2015  7 ; Nunan and Di Domenico, 2013  8 ; Anderson and Guyton, 2013  9 ) developments reflected in the evolving patterns of online social structures. These developments involve the ability of actors to adaptively regulate emotions, values, interest and demands between each other in an online social scene. In this paper, we provide a stateoftheart overview of approaches used in recognizing relational intelligence xe2x80x94 with special focus given to Online Social Networks (OSNs). The important core processes of data mining, identification (extraction), detection (labeling), classification, prediction and learning which empower machine recognition tasks will be discussed in detail. In addition, widely affected applications like recommending, ranking, influence, topic modeling, evolution, etc. will also be introduced along with their basic concepts uncovered to a detailed degree. We also include some discussions on more advanced topics that point to further interesting future research directions.

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.