# Graph Mining Published articles for Graph Mining. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-02-06T19:17:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Graphs](), [Tensorflow](), [Library](), [Google](), [Neural Network]() Tags: [google](), [graph](), [graph-mining](), [graphs](), [library](), [machine-learning](), [neural-networks](), [release](), [tensorflow]() ### AI overview Google Research announces TensorFlow GNN 1.0, a production-tested library for building and training graph neural networks at large scales. The library supports heterogeneous graphs, TensorFlow modeling and training, and extracting input graphs from large data stores. ### Source excerpt Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation -- take for example transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks as graphs, consisting of nodes connected by edges in various irregular ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scales. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs, where types of objects and relations are represented by distinct sets of nodes and edges. Real-world objects and their relations occur in distinct types, and TF-GNN's heterogen ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-02-06T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [ai](), [deep-learning](), [graph-mining](), [graphs](), [keras](), [learn](), [machine-learning](), [networks](), [python](), [release](), [release-notes](), [tensorflow](), [training]() ### AI overview Announces TensorFlow GNN 1.0, a production-tested library for building and training large-scale graph neural networks, including heterogeneous graphs. ### Source excerpt Posted by Dustin Zelle - Software Engineer, Research and Arno Eigenwillig - Software Engineer, CoreML This article is also shared on the Google Research Blog Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation -- for example: transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks them as graphs, consisting of nodes arbitrarily connected by edges in various irregular ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scale. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs where types and relations are represented by distinct sets of nodes and edges. Real-world objects and their relatio