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2024 | OriginalPaper | Buchkapitel

Interpreting Node Embedding Distances Through n-Order Proximity Neighbourhoods

verfasst von : Dougal Shakespeare, Camille Roth

Erschienen in: Complex Networks XV

Verlag: Springer Nature Switzerland

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Abstract

In the field of node representation learning the task of interpreting latent dimensions has become a prominent, well-studied research topic. The contribution of this work focuses on appraising the interpretability of another rarely-exploited feature of node embeddings increasingly utilised in recommendation and consumption diversity studies: inter-node embedded distances. Introducing a new method to measure how understandable the distances between nodes are, our work assesses how well the proximity weights derived from a network before embedding relate to the node closeness measurements after embedding. Testing several classical node embedding models, our findings reach a conclusion familiar to practitioners albeit rarely cited in literature—the matrix factorisation model SVD is the most interpretable through 1, 2 and even higher-order proximities.

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Metadaten
Titel
Interpreting Node Embedding Distances Through n-Order Proximity Neighbourhoods
verfasst von
Dougal Shakespeare
Camille Roth
Copyright-Jahr
2024
Buch
Complex Networks XV

Print ISBN: 978-3-031-57514-3

Electronic ISBN: 978-3-031-57515-0

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-57515-0_14

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