The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.
( Image credit: Fast Graph Representation Learning With PyTorch Geometric )
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Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep.
#7 best model for Node Classification on Cora
Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.
SOTA for Node Classification on Reddit
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.
Graph embedding has become a key component of many data mining and analysis systems.
Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification.
#2 best model for Graph Classification on FRANKENSTEIN
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals.
Existing node embedding models often suffer from a limitation of exploiting graph information to infer plausible embeddings of unseen nodes.
#21 best model for Node Classification on Pubmed