Development of the university image positioning methods in the context of its marketing strategy

Purpose: The note aims to study the steps actions algorithm in the positioning of university image, based on the method of using official media sites and searching systems and social networks. Design/Methodology/Approach: Today in the digital economy, higher educational establishments should take advantage of the opportunities arising from the involvement of the digital infrastructure in the process of their functioning. The positioning of an existing service or product usually takes the form of repositioning. The basis of the methodic apparatus of this study is the method of using official media sites and searching systems and social networks, which is used in the preparation of the steps algorithm in the university’s image positioning. Findings: The steps algorithm in the university image positioning is based on the method of using official media sites and searching systems and social networks. This method is developed by the authors and substantiated for the implementation in the sphere of higher educational establishments. Practical implications: In practice, we are talking about the formation of a mechanism function to provide the basic steps necessary to build an algorithm of the higher educational establishment image positioning. Originality/value: The concept "positioning" is the most often associated with persons (politicians, "stars") and companies engaged in the production or promotion of goods or services and positioning themselves to create their image, but not about the university positioning.peer-reviewe

Identity and Search in Social Networks

Social networks have the surprising property of being "searchable": Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases.Comment: 4 page, 3 figures, revte

A novel approach to study realistic navigations on networks

We consider navigation or search schemes on networks which are realistic in the sense that not all search chains can be completed. We show that the quantity $\mu = \rho/s_d$, where $s_d$ is the average dynamic shortest distance and $\rho$ the success rate of completion of a search, is a consistent measure for the quality of a search strategy. Taking the example of realistic searches on scale-free networks, we find that $\mu$ scales with the system size $N$ as $N^{-\delta}$, where $\delta$ decreases as the searching strategy is improved. This measure is also shown to be sensitive to the distintinguishing characteristics of networks. In this new approach, a dynamic small world (DSW) effect is said to exist when $\delta \approx 0$. We show that such a DSW indeed exists in social networks in which the linking probability is dependent on social distances.Comment: Text revised, references added; accepted version in Journal of Statistical Mechanic

Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources

Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks