<div class="csl-bib-body">
<div class="csl-entry">Arleo, A., Didimo, W., Liotta, G., Miksch, S., & Montecchiani, F. (2022). <i>Influence Maximization With Visual Analytics</i> [Conference Presentation]. IEEE Visualization & Visual Analytics 2022, Oklahoma City, United States of America (the). http://hdl.handle.net/20.500.12708/189760</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/189760
-
dc.description.abstract
In social networks, individuals’ decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; (ii) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms.
en
dc.language.iso
en
-
dc.subject
Information Visualization
en
dc.subject
Visualization systems and software
en
dc.subject
Information Diffusion
en
dc.subject
Influence Maximization
en
dc.subject
Visual Analytics
en
dc.title
Influence Maximization With Visual Analytics
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
University of Perugia, Italy
-
dc.contributor.affiliation
University of Perugia, Italy
-
dc.contributor.affiliation
University of Perugia, Italy
-
dc.type.category
Conference Presentation
-
tuw.researchTopic.id
I5
-
tuw.researchTopic.name
Visual Computing and Human-Centered Technology
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E193-07 - Forschungsbereich Visual Analytics
-
tuw.author.orcid
0000-0003-2008-3651
-
tuw.author.orcid
0000-0002-4379-6059
-
tuw.author.orcid
0000-0002-2886-9694
-
tuw.author.orcid
0000-0003-4427-5703
-
tuw.author.orcid
0000-0002-0543-8912
-
tuw.event.name
IEEE Visualization & Visual Analytics 2022
en
tuw.event.startdate
16-10-2022
-
tuw.event.enddate
21-10-2022
-
tuw.event.online
Hybrid
-
tuw.event.type
Event for scientific audience
-
tuw.event.place
Oklahoma City
-
tuw.event.country
US
-
tuw.event.presenter
Arleo, Alessio
-
dc.relation.ispreviousversionof
10.1109/TVCG.2022.3190623
-
tuw.event.track
Multi Track
-
wb.sciencebranch
Informatik
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.value
100
-
item.openairetype
conference paper not in proceedings
-
item.grantfulltext
restricted
-
item.openairecristype
http://purl.org/coar/resource_type/c_18cp
-
item.fulltext
no Fulltext
-
item.cerifentitytype
Publications
-
item.languageiso639-1
en
-
crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
-
crisitem.author.dept
University of Perugia
-
crisitem.author.dept
University of Perugia
-
crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
-
crisitem.author.dept
University of Perugia
-
crisitem.author.orcid
0000-0003-2008-3651
-
crisitem.author.orcid
0000-0002-4379-6059
-
crisitem.author.orcid
0000-0002-2886-9694
-
crisitem.author.orcid
0000-0003-4427-5703
-
crisitem.author.orcid
0000-0002-0543-8912
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
