Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

Using Topological Potential Method to Evaluate Node Importance in Public Opinion

Authors
Rui SUN, Wanbo LUO
Corresponding Author
Rui SUN
Available Online July 2017.
DOI
10.2991/eia-17.2017.75How to use a DOI?
Keywords
topological potential; node importance; public opinion; complex networks
Abstract

The evaluation of node importance is the main research direction in public opinion field, which is much significant to accurately find out the influent nodes for the propagation and evolution of public opinion, furthermore to effective control and predict public opinion situation and in-time guide it. On the basis of the topology structure of network and the attributes of node itself, this paper introduces a method to evaluate node importance in public opinion based on topological potential. Through the theoretical and experimental analysis, it is proved that this method can evaluate the importance of nodes in a fast and accurate way in propagation network of public opinion, which is significant both to theory and practice.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
Series
Advances in Intelligent Systems Research
Publication Date
July 2017
ISBN
10.2991/eia-17.2017.75
ISSN
1951-6851
DOI
10.2991/eia-17.2017.75How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Rui SUN
AU  - Wanbo LUO
PY  - 2017/07
DA  - 2017/07
TI  - Using Topological Potential Method to Evaluate Node Importance in Public Opinion
BT  - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
PB  - Atlantis Press
SP  - 349
EP  - 352
SN  - 1951-6851
UR  - https://doi.org/10.2991/eia-17.2017.75
DO  - 10.2991/eia-17.2017.75
ID  - SUN2017/07
ER  -