November 8, 2023 Bachelor Thesis Open Access

Oßwald, Paul

MARC21 XML Export

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    <subfield code="a">&lt;p&gt;In the last years, efforts have been made to use various machine learning models in the multiclass classification of the tt+X&amp;nbsp;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.&lt;/p&gt;

&lt;p&gt;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&amp;nbsp;events as input.&lt;/p&gt;</subfield>
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    <subfield code="a">Generating Explanations for Graph Neural Networks for tt+X events at the CMS Experiment</subfield>
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