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Math Behind Graph Neural Networks - Rishabh Anand

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Graph neural networks

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sl:bookmarkOf : https://rish-16.github.io/posts/gnn-math/
sl:creationDate : 2022-03-21
sl:creationTime : 2022-03-21T08:17:45Z

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Bookmark of: https://rish-16.github.io/posts/gnn-math/

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[Tweet](https://twitter.com/mickeyjs6/status/1253772146142216194?s=20) [GitHub](https://github.com/mickeystroller/TaxoExpan)

> we study the taxonomy expansion task: given an
existing taxonomy and a set of new emerging concepts, we aim
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> To the best of our knowledge, this is the first study on **how to
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Self-supervised framework, the existing taxonomy being used as training data: it learns a model to predict whether a query concept is the direct hyponym of an anchor concept.

> 2 techniques:
>
> 1. a **position-enhanced graph neural network that encodes the local structure of an anchor concept** in the existing taxonomy,
> 2. a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.

Regarding 1: uses [GNN](/tag/graph_neural_networks.html) to model the "ego network" of concepts (potential “siblings”
and “grand parents” of the query concept).

> Regular
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prediction performance

Regarding point 2: uses InfoNCE loss, cf. [Contrastive Predictive Coding](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1807.03748)

> Instead of predicting
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(Hum, ça me rappelle quelque chose)

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