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Graph neural networks

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27 Documents (Liste longue)

Tackling the Traveling Salesman Problem with Graph Neural Networks | by Michael Atkin | Stanford CS224W GraphML Tutorials | May, 2023 | Medium

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2023-05-18 A propos

[2301.08210] Everything is Connected: Graph Neural Networks

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2023-01-21 A propos

Graph representation learning in biomedicine and healthcare | Nature Biomedical Engineering

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2022-11-01 A propos

Using graph neural networks to recommend related products - Amazon Science

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2022-10-18 A propos

François Scharffe sur Twitter : "Exciting developments for query answering over graph neural networks" / Twitter

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2022-08-23 A propos

[2207.09980] ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

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2022-07-23 A propos

Math Behind Graph Neural Networks - Rishabh Anand

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Graph neural networks

2022-03-21 A propos

Link Prediction with Graph Neural Networks and Knowledge Extraction

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2021-09-03 A propos

A Gentle Introduction to Graph Neural Networks

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2021-09-03 A propos

[2011.02260] Graph Neural Networks in Recommender Systems: A Survey

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2020-11-11 A propos

[2010.05234] A Practical Guide to Graph Neural Networks

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2020-10-15 A propos

Petar Veličković sur Twitter : "resources I'd recommend for getting started with Graph Neural Nets (GNNs),

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2020-09-18 A propos

Latent graph neural networks: Manifold learning 2.0? | by Michael Bronstein | Sep, 2020

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2020-09-10 A propos

Graph Representation Learning Book - Will Hamilton

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2020-08-22 A propos

Aakash Kumar Nain sur Twitter : "I want to start reading about Graph NNs but I have two questions in my mind: 1. Applications of GNNs 2. Which paper should I start with?" / Twitter

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2020-05-11 A propos

Thomas Kipf's PhD thesis: "Deep Learning with Graph-Structured Representations"

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2020-05-05 A propos

[2001.09522] TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

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how to add a set of new concepts to an existing taxonomy.

[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
to automatically expand the taxonomy to incorporate these new
concepts (without changing the existing relations in the given taxonomy).

> To the best of our knowledge, this is the first study on **how to
expand an existing directed acyclic graph (as we model a taxonomy
as a DAG) using self-supervised learning**.

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
GNNs fail to distinguish nodes with different relative positions to
the query (i.e., some nodes are grand parents of the query while
the others are siblings of the query). To address this limitation, we
present a simple but effective enhancement to inject such position
information into GNNs using position embedding. We show that
such embedding can be easily integrated with existing GNN architectures
(e.g., [GCN](/tag/graph_convolutional_networks) and GAT) and significantly boosts the
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
whether each individual ⟨query concept, anchor concept⟩ pair
is positive or not, we first group all pairs sharing the same query
concept into a single training instance and learn a model to select
the positive pair among other negative ones from the group.

(Hum, ça me rappelle quelque chose)

> assume each concept (in existing taxonomy + set of new concepts) has an initial embedding
vector learned from some text associated with this concept.

To keep things tractable, only attempts to find a single parent node of each new concept.

2020-04-25 A propos

Combining knowledge graphs, quickly and accurately (2020)

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2020-03-19 A propos

[2003.00330] Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

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2020-03-15 A propos

Chaitanya Joshi sur Twitter : "Excited to share a blog post on the connection between #Transformers for NLP and #GraphNeuralNetworks"

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2020-03-01 A propos

Transformers are Graph Neural Networks | NTU Graph Deep Learning Lab

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2020-03-01 A propos

Graph Neural Networks for Natural Language Processing tutorial at EMNLP 2019

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2019-11-08 A propos

[1901.00596] A Comprehensive Survey on Graph Neural Networks

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2019-07-15 A propos

A Neural QA Model for DBpedia (GSoC 2019)

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2019-03-26 A propos

A Gentle Introduction to Graph Neural Network (Basics, DeepWalk, and GraphSage)

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Graph neural networks

2019-03-09 A propos

dmlc/dgl: Python package built to ease deep learning on graph, on top of existing DL frameworks.

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2018-12-12 A propos

[1806.01261] Relational inductive biases, deep learning, and graph networks

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2018-06-13 A propos

Propriétés

sl:creationDate : 2018-06-13
sl:creationTime : 2018-06-13T13:38:36Z
rdf:type : sl:Tag
skos:altLabel : GNN@en
skos:prefLabel : Graph neural networks