DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Using Link Clustering to Detect Influential Spreaders

Krukowski, Simon and Hecking, Tobias (2020) Using Link Clustering to Detect Influential Spreaders. In: 9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020. Springer Nature. 9th International Conference on Complex Networks and their Applications, 2020-12-01 - 2020-12-03, Madrid, Spanien. doi: 10.1007/978-3-030-65347-7_34. ISBN 978-3-030-65346-0. ISSN 1860-949X.

Full text not available from this repository.


Spreading processes are increasingly analysed in the context of complex networks, for example in epidemics research, information dissemination or rumors. In these contexts, the effect of structural properties that facilitate or decelerate spreading processes are of high interest, since this allows insights into the extent to which those processes are controllable and predictable. In social networks, actors usually participate in different densely connected social groups that emerge from various social contexts, such as workplace, interests, etc. In this paper, it is examined if the number of groups an actor connects to can be used as a predictor for its capability to spread information effectively. The social contexts (i.e. groups) a node participates in are determined by the Link Communities approach by Ahn et al. (2010). The results are contrasted to previous findings of structural node properties based on the k-shell index of nodes (Kitsak et al. 2010) by applying both methods on artificially generated and real-world networks. They show that the approach is comparable to existing ones using structural node properties as a predictor, yet no clear evidence is found indicating that one or the other approach leads to better predictions in all investigated networks.

Item URL in elib:https://elib.dlr.de/139522/
Document Type:Conference or Workshop Item (Speech)
Title:Using Link Clustering to Detect Influential Spreaders
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hecking, TobiasUNSPECIFIEDhttps://orcid.org/0000-0003-0833-7989UNSPECIFIED
Journal or Publication Title:9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Publisher:Springer Nature
Keywords:Link clustering, Spreading processes, Information diffusion
Event Title:9th International Conference on Complex Networks and their Applications
Event Location:Madrid, Spanien
Event Type:international Conference
Event Start Date:1 December 2020
Event End Date:3 December 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben SISTEC (old)
Location: Köln-Porz
Institutes and Institutions:Institut of Simulation and Software Technology > Distributed Systems and Component Software
Institute for Software Technology
Deposited By: Hecking, Dr. Tobias
Deposited On:14 Dec 2020 09:18
Last Modified:24 Apr 2024 20:40

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.