A Temporal Graphlet Kernel For Classifying Dissemination in Evolving Networks

Lutz Oettershagen, Nils Morten Kriege, Claude Jordan, Petra Mutzel

Veröffentlichungen: Beitrag in BuchBeitrag in KonferenzbandPeer Reviewed

Abstract

We introduce the temporal graphlet kernel for classifying dissemination processes in labeled temporal graphs. Such processes can be the spreading of (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks are modeled as labeled temporal graphs, in which the edges exist at specific points in time, and node labels change over time. The classification problem asks to discriminate dissemination processes of different origins or parameters, e.g., diseases with different infection probabilities. Our new kernel represents labeled temporal graphs in the feature space of temporal graphlets, i.e., small subgraphs distinguished by their structure, time-dependent node labels, and chronological order of edges. We introduce variants of our kernel based on classes of graphlets that are efficiently countable. For the case of temporal wedges, we propose a highly efficient approximative kernel with low error in expectation. Our experimental evaluation shows that our kernels are computed faster than state-of-the-art methods and provide higher accuracy in many cases.
OriginalspracheEnglisch
TitelProceedings of the 2023 SIAM International Conference on Data Mining (SDM)
Seiten19-27
ISBN (elektronisch)978-1-61197-765-3
DOIs
PublikationsstatusVeröffentlicht - 27 Apr. 2023
VeranstaltungSIAM International Conference on Data Mining (SDM23) - Minneapolis, USA / Vereinigte Staaten
Dauer: 27 Apr. 202329 Apr. 2023
https://www.siam.org/conferences/cm/conference/sdm23

Konferenz

KonferenzSIAM International Conference on Data Mining (SDM23)
Land/GebietUSA / Vereinigte Staaten
OrtMinneapolis
Zeitraum27/04/2329/04/23
Internetadresse

ÖFOS 2012

  • 102019 Machine Learning

Fingerprint

Untersuchen Sie die Forschungsthemen von „A Temporal Graphlet Kernel For Classifying Dissemination in Evolving Networks“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitationsweisen