Efficient algorithm for finding the influential nodes using local relative change of average shortest path

Koduru Hajarathaiah, Murali Krishna Enduri and Satish Anamalamudi

Physica A: Statistical Mechanics and its Applications, 2022, vol. 591, issue C

Abstract: In complex networks, finding the influential nodes playing a crucial role in theoretical and practical point of view because they are capable of propagating information to large portion of the network. Investigating the dynamics of information spreading in complex networks is a hot topic with a wide range of applications, including information dissemination, information propagation, rumour control, viral marketing, and opinion monitoring. In recent years, several centrality measures have been discovered to find influential nodes in complex networks. In this work, the local relative change of average shortest path (i.e Local RASP) based on the local structure of the network is being proposed. This local RASP measure of a node defined based on the local network’s relative change in average shortest path when the node is deleted. Our local RASP centrality produces good results compared to degree, betweenness, closeness, semi-local, PageRank, Trust-PageRank, and RASP centralities. Our local RASP centrality measure’s computation time is less compared to global centrality measure RASP. It measures the information diffusion efficiently within the network through the initial seed nodes identified by the local RASP.

Keywords: Influential nodes; Local centrality; Average shortest path; Complex networks (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437121009262
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:591:y:2022:i:c:s0378437121009262

DOI: 10.1016/j.physa.2021.126708

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().