<div class="csl-bib-body">
<div class="csl-entry">Thiessen, M., & Gärtner, T. (2020). Active Learning on Graphs with Geodesically Convex Classes. In <i>Proceedings of 16th International Workshop on Mining and Learning with Graphs (MLG’20)</i>. 16th International Workshop on Mining and Learning with Graphs, Austria. https://doi.org/10.34726/3467</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/144320
-
dc.identifier.uri
https://doi.org/10.34726/3467
-
dc.description.abstract
We study the problem of actively learning the vertex labels of a graph, assuming the classes form geodesically convex subgraphs, which is related to linear separability in the Euclidean setting. The main result of this paper is a novel query-efficient active learning algorithm with label-independent upper bounds on the number of queries needed to learn all labels. For that, we use shortest path covers and provide a logarithmic approximation for the sub-problem of computing a shortest path cover of minimum size. We extend the approach to arbitrarily labeled graphs using a convexity-based selection criterion. Finally, we discuss whether the convexity assumption holds on real-world data and give some first preliminary results on citation and image benchmark datasets.
en
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Machine Learning
en
dc.subject
active learning
en
dc.subject
graphs
en
dc.subject
geodesic convexity
en
dc.subject
node classification
en
dc.subject
multi-class
en
dc.subject
path cover
en
dc.title
Active Learning on Graphs with Geodesically Convex Classes
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/3467
-
dc.type.category
Poster Contribution
-
tuw.booktitle
Proceedings of 16th International Workshop on Mining and Learning with Graphs (MLG'20)
-
tuw.peerreviewed
true
-
tuw.researchTopic.id
I4a
-
tuw.researchTopic.name
Information Systems Engineering
-
tuw.researchTopic.value
100
-
tuw.linking
http://www.mlgworkshop.org/2020/
-
tuw.linking
https://www.youtube.com/watch?v=MsxJT2cP8yg
-
tuw.publication.orgunit
E194-06 - Forschungsbereich Machine Learning
-
dc.identifier.libraryid
AC17202964
-
tuw.author.orcid
0000-0001-9333-2685
-
tuw.author.orcid
0000-0001-5985-9213
-
dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.event.name
16th International Workshop on Mining and Learning with Graphs
-
tuw.event.startdate
24-08-2020
-
tuw.event.enddate
24-08-2020
-
tuw.event.online
Online
-
tuw.event.type
Event for scientific audience
-
tuw.event.country
AT
-
tuw.event.presenter
Thiessen, Maximilian
-
tuw.presentation.online
Online
-
wb.sciencebranch
Informatik
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.value
100
-
item.openaccessfulltext
Open Access
-
item.cerifentitytype
Publications
-
item.languageiso639-1
en
-
item.openairecristype
http://purl.org/coar/resource_type/c_6670
-
item.grantfulltext
open
-
item.fulltext
with Fulltext
-
item.mimetype
application/pdf
-
item.openairetype
conference poster
-
crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.orcid
0000-0001-9333-2685
-
crisitem.author.orcid
0000-0001-5985-9213
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering