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Questions and Answers
In graph theory, how many nodes does a single edge connect?
Which of the following tasks do Graph Neural Networks (GNNs) typically struggle with?
In the context of cell complexes, what does a p-cell represent?
What does the acronym FORGE stand for in the context of graph learning?
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After applying FORGE, how do explainers perform compared to Random baselines?
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Based on the lecture content, what can the boundary relation be loosely translated to in graph theory?
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What does guardedness mean as discussed in the lecture?
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What is the process/transformation used to achieve guardedness?
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How are steering vectors generally defined as discussed in the lecture?
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Which of the following is a limitation of graphs as a data structure?
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In the context of the paper SaGE, what is semantic consistency?
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Why do models struggle with tasks in 'moral scenarios'?
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What metric was used to determine the quality of the paraphrase of the questions in the SaGE paper?
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Study Notes
Graph Theory
- An edge connects two nodes.
Graph Neural Networks (GNNs)
- GNNs typically struggle with cycle detection.
Cell Complexes
- A p-cell represents an element of dimension p.
FORGE (Framework for Higher-Order Representations in Graph Explanations)
- FORGE is a framework for explaining higher-order representations in graph explanations.
- Explainer performance consistently surpasses random baselines after applying FORGE.
Boundary Relation
- The boundary relation in graph theory can be loosely translated to edges.
Guardedness
- Guardedness refers to an attribute that cannot be used for classification.
Affine Concept Erasure
- Affine Concept Erasure is used to achieve guardedness.
Steering Vectors
- Steering Vectors are generally defined as v = μ0 - μ1, where μ0 is the mean of the desirable class, and μ1 is the mean of the undesirable class.
Limitation of Graphs as a Data Structure
- Graphs can only model pairwise relationships between nodes.
SaGE (Semantic Graph Embedding)
- Semantically equivalent questions should yield semantically equivalent answers.
Challenges with Moral Scenarios in Models
- Conflicting training data due to different morals that people have can make models struggle in "moral scenarios."
Determining Quality of Question Paraphrases in SaGE
- Paraphrases were evaluated using Parascore.
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Description
This quiz covers key concepts in graph theory and their application in graph neural networks. Explore important terms such as GNNs, cell complexes, and more specialized frameworks like FORGE. Test your understanding of these advanced topics in the realm of graph representation and analysis.
