Abstract
A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essential features of the model. The same influence spreading model is used for community detection in social networks and for analysis of network structures. Weaker connections give rise to more sub-communities whereas stronger ties increase the cohesion of a community. The validity of the method is demonstrated with different social networks. Our model takes into account different paths between nodes in the network structure. The dependency of different paths having common links at the beginning of their paths makes the model more realistic compared to classical structural, simulation and random walk models. The influence of all nodes in a network has not been satisfactorily understood. Existing models may underestimate the spreading power of interconnected peripheral nodes as initiators of dynamic processes in social, biological and technical networks.
Highlights
The aim of this paper is to provide answers to the requirements presented in the previous section
The same influence spreading model is used for community detection in social networks and for analysis of network structures
Random walks consider different paths from a source node to a target node but the method still is unsuccessful in combining the contributions from alternative paths to generate an exact quantitative measure
Summary
The aim of this paper is to provide answers to the requirements presented in the previous section. Social influence measures have been developed by using for example local structural characteristics [1, 2] geodesic distances [3] and random walks [4]. Most of these measures don’t have exact quantitative interpretations for general network structures and variable sizes of networks. Random walks consider different paths from a source node to a target node but the method still is unsuccessful in combining the contributions from alternative paths to generate an exact quantitative measure.
Sign-in/Register to view highlights & summary 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.
