Representation Learning on Graphs and Networks

Graph Representation Learning - SpringerLink

Different from previous methods that use shallow neural networks to characterize the graph representations, structural deep network embedding ( ...

Learning on Graphs Conference

Local Meetups. At the same time as the main virtual event, the LoG community hosted a 'network' of local mini-conferences around the world ...

Machine Learning with Graphs (NETS 7332) - Tina Eliassi-Rad

Here are some textbooks (all optional) on machine learning and data mining: Deep Learning and Graph Representation Learning. Charu C. Aggarwal. Neural Networks ...

Graph representation learning: a survey

This idea is elaborated in Section III, C. • Neural networks: Neural network models such as convolution neural net- work (CNN) [35], recursive neural networks ...

Graph-level Representation Learning with Joint-Embedding ...

This work proposes a new self-supervised technique for graph neural networks. Grounded on joint-embedding predictive architecture (JEPAs), the ...

Graph Representation Learning and Graph Classification

The more recent approach is to use neural networks to learn a latent representation. In these approaches the neural network is responsible for extracting the ...

Graph Neural Network for representation learning of lung cancer

Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach.

Graph neural network - Wikipedia

A graph neural network (GNN) belongs to a class of artificial neural networks for processing data that can be represented as graphs.

Graph Representation Learning - Jian Tang

Impact! ▫ Social networking, Social media, Drug design. Tutorial on Graph Representation Learning, AAAI 2019. 7 ...

Introduction to Graph Representation Learning | K. Kubara

Graph Neural Networks. One of the first graph neural network architectures created by Duvenaud et al. It is a type of Message ...

Graph Representation Learning and Its Applications: A Survey - MDPI

Graph neural networks (GNNs) have shown a significant expressive capacity to represent graph embeddings in an inductive learning manner and solve the ...

Editorial: Graph representation learning in biological network

This article is part of the Research Topic Graph Representation Learning in Biological Networks View all 5 articles

Introduction to Graph Machine Learning - Hugging Face

Neural networks can generalise to unseen data. Given the representation constraints we evoked earlier, what should a good neural network be to ...

All-optical graph representation learning using integrated diffractive ...

Photonic neural networks perform brain-inspired computations using photons instead of electrons to achieve substantially improved computing ...

A Comprehensive Survey on Deep Graph Representation Learning ...

Representation Learning, Deep Learning for Graphs, Graph Neural Network, Graph Embedding ... Graph Neural Networks' drawbacks, and suggests ...

Graph Representation Learning | PPT - SlideShare

Feature Learning in Graphs This talk: Feature learning for networks! Jure Leskovec, Stanford.

Empowering Graph Representation Learning with Test-Time Graph...

Transforming the test graph data can enhance the generalization and robustness of graph neural networks.

Deep Graph Library

heterogeneous graph, link prediction, large-scale. Representation Learning on Graphs with Jumping Knowledge Networks, message passing, neighborhood. RotatE ...

Hierarchical Graph Representation Learning with Differentiable ...

Authors. Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec. Abstract. Recently, graph neural networks (GNNs) have ...

Geometry-enhanced molecular representation learning for property ...

Recent advances for molecular representation learning have shown great promise in applying graph neural networks to model molecules. Moreover, a ...