---
res:
bibo_abstract:
- "Graphs are a flexible and general formalism providing rich models in various
important domains, such as distributed computing, intelligent tutoring systems
or social network analysis. In many cases, such models need to take changes in
the graph structure into account, that is, changes in the number of nodes or in
the graph connectivity. Predicting such changes within graphs can be expected
to yield important insight with respect to the underlying dynamics, e.g. with
respect to user behaviour. However, predictive techniques in the past have almost
exclusively focused on single edges or nodes. In this contribution, we attempt
to predict the future state of a graph as a whole.\r\nWe propose to phrase time
series prediction as a regression problem and apply dissimilarity- or kernel-based
regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian
process regression, which can be applied to graphs via graph kernels. The output
of the regression is a point embedded in a pseudo-Euclidean space, which can be
analyzed using subsequent dissimilarity- or kernel-based processing methods. We
discuss strategies to speed up Gaussian Processes regression from cubic to linear
time and evaluate our approach on two well-established theoretical models of graph
evolution as well as two real data sets from the domain of intelligent tutoring
systems. We find that simple regression methods, such as kernel regression, are
sufficient to capture the dynamics in the theoretical models, but that Gaussian
process regression significantly improves the prediction error for real-world
data.@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Benjamin
foaf_name: Paaßen, Benjamin
foaf_surname: Paaßen
foaf_workInfoHomepage: http://www.librecat.org/personId=49941564
orcid: 0000-0002-3899-2450
- foaf_Person:
foaf_givenName: Christina
foaf_name: Göpfert, Christina
foaf_surname: Göpfert
foaf_workInfoHomepage: http://www.librecat.org/personId=70126969
orcid: 0000-0003-2517-4907
orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-2517-4907/work/37593284
- foaf_Person:
foaf_givenName: Barbara
foaf_name: Hammer, Barbara
foaf_surname: Hammer
foaf_workInfoHomepage: http://www.librecat.org/personId=18302458
bibo_doi: 10.1007/s11063-017-9684-5
bibo_issue: '2'
bibo_volume: 48
dct_date: 2018^xs_gYear
dct_identifier:
- UT:000446501500003
dct_isPartOf:
- http://id.crossref.org/issn/1370-4621
- http://id.crossref.org/issn/1573-773X
dct_language: eng
dct_publisher: Springer@
dct_subject:
- Structured Data
- Graphs
- Time Series Prediction
- Gaussian Processes
- Kernel Space
dct_title: Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces@
...