Abstract
Symmetry is one of the important properties of Social networks to indicate the co-existence relationship between two persons, e.g., friendship or kinship. Centrality is an index to measure the importance of vertices/persons within a social network. Many kinds of centrality indices have been proposed to find prominent vertices, such as the eigenvector centrality and PageRank algorithm. PageRank-based algorithms are the most popular approaches to handle this task, since they are more suitable for directed networks, which are common situations in social media. However, the realistic problem in social networks is that the process to find true important persons is very complicated, since we should consider both how the influence of a vertex affects others and how many others follow a given vertex. However, past PageRank-based algorithms can only reflect the importance on the one side and ignore the influence on the other side. In addition, past algorithms only view the transition from one status to the next status as a linear process without considering more complicated situations. In this paper, we develop a novel centrality to find key persons within a social network by a proposed synthesized index which accounts for both the inflow and outflow matrices of a vertex. Besides, we propose different transition functions to represent the relationship from status to status. The empirical studies compare the proposed algorithms with the conventional algorithms and show the differences and flexibility of the proposed algorithm.
Highlights
Key person identification within a social network means to find persons who can change the feelings, attitudes, or behaviors of other persons though network relationships [1] and, this is a critical issue in the fields of viral marketing [2], spread of opinions [3], rumor restraint [3], and innovation dissemination [4]
We should consider more important factors which are not accounted for by PageRank-based algorithms to determine which persons are prominent within a social network
The empirical results indicate that the proposed algorithm is flexible and that the derived centrality can be considered as a synthesized index to determine key persons within a social network
Summary
Key person identification within a social network means to find persons who can change the feelings, attitudes, or behaviors of other persons though network relationships [1] and, this is a critical issue in the fields of viral marketing [2], spread of opinions [3], rumor restraint [3], and innovation dissemination [4]. Many algorithms have been proposed to identify important persons within a social network based on the concept of vertex centralities. Vertex centrality measures the importance of persons within a network according to their position relative to others These measures can be divided into local measures, short path-based measures, and iterative calculation-based measures [5]. We should consider more important factors which are not accounted for by PageRank-based algorithms to determine which persons are prominent within a social network. The iterative process of calculating the centrality in PageRank-based algorithms is a linear transition and ignores the possibility of non-linear functions These algorithms usually normalize the centrality by dividing the out-degree of the ego. The empirical results indicate that the proposed algorithm is flexible and that the derived centrality can be considered as a synthesized index to determine key persons within a social network
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
