120 papers with code ·
Graphs

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 )

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

We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.

#10 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.

GRAPH CLASSIFICATION KNOWLEDGE GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

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.

COMMUNITY DETECTION GRAPH CLUSTERING NETWORK EMBEDDING NODE CLASSIFICATION

Graph embedding has become a key component of many data mining and analysis systems.

COMMUNITY DETECTION GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION STOCHASTIC OPTIMIZATION

Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations.

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