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Dec 14, 2017 · Large-scale graph representation learning Abstract: Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.

Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation ...

Jure LeskovecStanford University. Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph.

16.11.2020 · A Large-Scale Database for Graph Representation Learning Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau With the rapid emergence of graph representation learning, the construction of new large-scale datasets are necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique.

In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning ...

Nov 16, 2020 · Abstract: With the rapid emergence of graph representation learning, the construction of new large-scale datasets are necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs, and (3) class diversity.

The majority of these methods do not scale to large graphs or are designed for whole-graph classification (or both) [4, 9, 8, 24]. However, our approach is ...

Jul 20, 2021 · Large-scale graph representation learning with very deep GNNs and self-supervision. Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning.

20.07.2021 · Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions have only ever been validated on comparatively small datasets, often with counter-intuitive outcomes -- a barrier which has been broken by the Open Graph Benchmark Large-Scale …

07/20/21 - Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph ...

14.12.2017 · Large-scale graph representation learning Abstract:Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.

Library for deep learning on graphs. ... a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, ...

Abstract: Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs.

Abstract Large-scale Graph Representation Learning Jure Leskovec Stanford University Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.

Jul 20, 2021 · Large-scale graph representation learning with very deep GNNs and self-supervision. Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions have only ever been validated on comparatively small datasets, often with counter ...

12.02.2021 · Large-Scale Representation Learning on Graphs via Bootstrapping Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Veličković, Michal Valko Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs.

We provide a detailed analysis of MALNET, discussing its properties and provenance, along with the evaluation of state-of-the-art machine learning and graph ...

Feb 12, 2021 · Large-Scale Representation Learning on Graphs via Bootstrapping Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Veličković, Michal Valko Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs.

27.07.2021 · Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions have only ever been validated on comparatively small datasets, often with counter-intuitive outcomes---a barrier which has been broken by the Open Graph Benchmark Large-Scale …