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Suppression of Information Diffusion in Social Network Using Centrality based on Dynamic Process

Authors:
Eiichi Takazawa
Norihiko Shinomiya

Keywords: Centrality, Graph Theory, Information Diffusion

Abstract:
Individual activities propagate on social networks and had a large impact on our society. For example, incitement acts such as hoaxes, widely propagated through social media, gave unnecessary confusion and uneasiness to many people. The purpose of this study is to propose an edge centrality index in a network considering the propagation of activities through analysis. Our previous studies have proposed an evaluation method that quantifies the edge importance based on an activity propagation model. The model represents the propagation by an equalization process of the variable amount given to each vertex. This paper experimentally shows that the information diffusion can be suppressed by using the edge importance measure. The experiment verifies that the range of information diffusion becomes smaller than that before deleting some edges from the network based on the importance measure.

Pages: 206 to 209

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-686-6

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019