Shypd.aiThe largest open index of AI toolsSEAL is a Data & Analytics tool that transforms link prediction into a subgraph classification problem. It leverages subgraphs, embeddings, and attributes for improved prediction accuracy using graph neural networks.
Trending
About
SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction) is a novel framework designed for link prediction. It systematically transforms the link prediction task into a subgraph classification problem. For each target link, SEAL extracts its h-hop enclosing subgraph and constructs a node information matrix, which can include structural node labels, latent embeddings, and explicit attributes. This data is then fed into a graph neural network (GNN) to classify the existence of the link, allowing the model to learn from both graph structure features and latent/explicit node features simultaneously. The framework is implemented in both MATLAB and Python, with a PyTorch Geometric version available for testing on OGB, Planetoid, and custom datasets. Notably, SEAL can achieve strong performance even without node embeddings or attributes, leveraging purely graph structures, and can function as an inductive link prediction model.
Capabilities
Pricing & Plans
Open Source
Free
FAQs
Trending
Also listed in
