Bachelor Thesis Open Access

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

Oßwald, Paul

JSON-LD (schema.org) Export

{
  "@context": "https://schema.org/", 
  "@type": "ScholarlyArticle", 
  "contributor": [], 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "KIT/ETP", 
      "name": "O\u00dfwald, Paul"
    }
  ], 
  "datePublished": "2023-11-08", 
  "description": "<p>In the last years, efforts have been made to use various machine learning models in the multiclass classification of the tt+X&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.</p>\n\n<p>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&nbsp;events as input.</p>", 
  "headline": "Generating Explanations for Graph Neural Networks for tt+X events at the CMS Experiment", 
  "image": "https://publish.etp.kit.edu/static/img/logos/zenodo-gradient-round.svg", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "Graph Neural Networks", 
    "GNNExplainer", 
    "CMS", 
    "ttX", 
    "Machine learning"
  ], 
  "name": "Generating Explanations for Graph Neural Networks for tt+X events at the CMS Experiment", 
  "url": "https://publish.etp.kit.edu/record/22217"
}
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:

Cite as

Export