Announcing the release of TensorFlow GNN 1.0, a production-tested library for building GNNs at Google scale, supporting both modeling and training.
A guide to GNNs with self-attention using PyTorch Geometric
A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operation called negative sampling. Usually, the purpose of the learning objective is to maximize the similarity between neighboring nodes while minimizing the similarity between nodes that are not close to each other. Negative sampling can reduce the time complexity by sampling a small fraction of the negative nodes instead of using all of the negative nodes when optimizing the objective. However, sampling of the negative nodes may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such disadvantages, we provide an efficient Non-Sampling Graph Neural Network (NS-GNN) framework. The main idea is to use all the negative samples when optimizing the learning objective to avoid the sampling process. Of course, directly using all of the negative samples may cause a large increase in the model training time. To mitigate this problem, we rearrange the origin loss function into a linear form and take advantage of meticulous mathematical derivation to reduce the complexity of the loss function. Experiments on benchmark datasets show that our framework can provide better efficiency at the same level of prediction accuracy compared with existing negative sampling-based models.
Adaptive Dependency Learning Graph Neural Networks
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.
Under a microscope, a pane of window glass doesn’t look like a collection of orderly molecules, as a crystal would, but rather a jumble with no discernable structure. Glass is made by starting...
This PrimeView highlights future applications of graph neural networks.
The evaluation was done using two tools: calibration or reliability diagram and the metric expected calibration error(ECE).
Ayyoob Imani, Lütfi Kerem Senel, Masoud Jalili Sabet, François Yvon, Hinrich Schuetze. Findings of the Association for Computational Linguistics: ACL 2022. 2022.
Abstract page for arXiv paper 2202.08833: What Functions Can Graph Neural Networks Generate?
Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how to build a Graph Neural Network with Pytorch.
Up until recently, practitioners would use molecular fingerprints (essentially one-hot encodings of different molecular substructures) as...
Graph Commons is a collaborative platform for mapping, analyzing, and sharing data-networks
The number of tools involving deep learning in the drug discovery domain is growing exponentially. Recent advances make use of graph representations of molecules and graph neural network-based deep l...
Graph Intelligence will be a sizable percentage of the $299B AI market. Graph Intelligence architectures are converging to patterns that largely complement modern data platforms. You don’t need a graph database: none of Meituan’s 30 GNNs use one. Our recommendation for most companies starting out is to jump right in. It’s just a matter of time until graphs become a standard method to analyze data for operational insights. Figure 5 shows that tools are now in place for most companies to get started.
VC dimension of Graph Neural Networks with Pfaffian activation functions
Social media has become popular in recent years, and users are connected to each other through followers-followers relationships. There are many other types of data that have a network structure in…
The expressive power of graph neural networks has deep relations to classical results in graph theory, described in this post.
Understanding interparticle interactions has been one of the most important topics of research in the field of micro/nanoscale materials. Many significant chara
