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 ...
@cite_15: Together with the sign (positive or negative) and strength (strong or weak), the directionality is also an important property of social ties, though usually ignored in undirected social networks for its invisibility. However, we believe most social ties are natively directed, and the awareness of directionality can improve our understanding about the network structures and further benefit social network analysis and mining tasks. Thus it's appealing to study whether there exist interesting patterns about directionality in social networks and whether we can learn the directions for undirected networks based on these patterns. In this study, we engage in the investigation of directionality patterns on real-world directed social networks and summarize our findings using four consistency hypotheses. Based on these hypotheses, we propose ReDirect, an optimization framework which makes it possible to infer the hidden directions of undirected social ties based on the network topology only. This general framework can incorporate various predictive models under specific scenarios. Furthermore, we show how to improve ReDirect by introducing semi self-supervision in the framework and how to construct the self-labeled training data using simple but effective heuristics. Experimental results show that even without external information, our approach can recover the directions of networks effectively. Moreover, we're quite surprising to find that ReDirect can benefit predictive tasks remarkably, with a case study of link prediction. In experiments the redirected networks inferred using ReDirect are proven much more informative than original undirected ones and can improve the prediction performance significantly. It convinces us that ReDirect can be a beneficial general data preprocess tool for various network analysis and mining tasks by uncovering the hidden directions of undirected social networks.
@cite_16: 
@cite_10: Purpose – To provide an interesting approach for determining interval measures, through the analytic hierarchy process, for integration with social network analysis for knowledge mapping in organizations.Design methodology approach – In order to develop improved organizational and business processes through knowledge management, a knowledge audit should be conducted to better understand the knowledge flows in the organization. An important technique to visualize these knowledge flows is the use of a knowledge map. Social network analysis can be applied to develop this knowledge map. Interval measures should be used in the social network analysis in order to determine the strength of the connections between individuals or departments in the organization. This paper applies the analytic hierarchy process to develop these interval measures, and integrates the values within the social network analysis to produce a meaningful knowledge map.Findings – The analytic hierarchy process, when coupled with social net...
More recently, @cite_15 argue that not only the sign and strength are important when it comes to obtaining a better understanding of social network structure, but also the direction of the ties between the actors. Liebowitz @cite_10 integrates the usage of an analytical hierarchical process with social network analysis on the organizational level to create a knowledge map. Moreover, @cite_16 adopt the principle of social stratification in their approach, which refers to the categorization of people in society into ranked groups based on their status, power, wealth or knowledge. By applying stratification on humans, they assumed that people who are higher up in the hierarchy tend to have a higher status (ranking) in comparison to people who are lower in the hierarchy. Further, people at the top levels of the hierarchy are less likely to connect to people at low levels of the hierarchy. An algorithm has been suggested to find the best hierarchy in a directed network. However, they have not considered the time dimension in the problem. Moreover, they study the problem at a network-level and not at an actor-level as in this study.