GNNs Insights | Sumble

What is GNNs?

Graph Neural Networks (GNNs) are a class of neural networks designed to operate on graph-structured data. They learn representations of nodes, edges, or entire graphs by iteratively aggregating and transforming information from their neighbors. GNNs are commonly used in various applications such as node classification, link prediction, graph classification, and graph generation. Examples include social network analysis, drug discovery, recommendation systems, and traffic prediction.

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What other technologies are related to GNNs?

GNNs Competitor Technologies

RNNs

Recurrent Neural Networks are used for sequential data, and GNNs are used for graph-structured data. They are alternative approaches for different data types.

mentioned alongside GNNs in 5% (84) of relevant job posts

CNNs

Convolutional Neural Networks are mainly used for image data, while GNNs are used for graph-structured data. They solve similar problems but with different data representations. Can be used as feature extractors within GNNs in certain applications.

mentioned alongside GNNs in 4% (96) of relevant job posts

Transformers

Transformers are primarily used for sequence-to-sequence tasks, particularly in NLP, but are increasingly being applied to graph data. They offer an alternative approach to GNNs for some graph-related tasks.

mentioned alongside GNNs in 1% (187) of relevant job posts