Bachelor Thesis Open Access

Generating Explanations for Graph Neural Networks for tt+X events at the CMS Experiment

Oßwald, Paul

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Oßwald, Paul</dc:creator>
  <dc:date>2023-11-08</dc:date>
  <dc:description>In the last years, efforts have been made to use various machine learning models in the multiclass classification of the tt+X events. Especially Graph Neural Networks (GNN) architectures have received attention, due to the data structure lending itself to the representation as a multi-relational graph. Naturally, when using a GNN to classify an event, it is of interest why it makes a particular prediction. GNNs however, due to the complex structure of graph data and black-box nature, are hard to interpret.

This thesis focuses on examining a GNN trained for this classification task using the GNNExplainer method to better understand the underlying decisions taken by the GNN when using different tt+X events as input.</dc:description>
  <dc:identifier>https://publish.etp.kit.edu/record/22217</dc:identifier>
  <dc:identifier>oai:publish.etp.kit.edu:22217</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://publish.etp.kit.edu/communities/cms</dc:relation>
  <dc:relation>url:https://publish.etp.kit.edu/communities/etp</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>Graph Neural Networks</dc:subject>
  <dc:subject>GNNExplainer</dc:subject>
  <dc:subject>CMS</dc:subject>
  <dc:subject>ttX</dc:subject>
  <dc:subject>Machine learning</dc:subject>
  <dc:title>Generating Explanations for Graph Neural Networks for tt+X events at the CMS Experiment</dc:title>
  <dc:type>info:eu-repo/semantics/bachelorThesis</dc:type>
  <dc:type>thesis-bachelor-thesis</dc:type>
</oai_dc:dc>
Publication date:
November 8, 2023
Keyword(s):
Graph Neural Networks GNNExplainer CMS ttX Machine learning
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-BACHELOR-KA/2023-13
Communities:

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