Node Classification
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.
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Latest papers without code
HONEM: Network Embedding Using Higher-Order Patterns in Sequential Data
We demonstrate that the higher-order network embedding (HONEM) method is able to extract higher-order dependencies from HON to construct the higher-order neighborhood matrix of the network, while existing methods are not able to capture these higher-order dependencies.
FEATURE ENGINEERING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
End-to-End Learning from Complex Multigraphs with Latent Graph Convolutional Networks
We study the problem of end-to-end learning from complex multigraphs with potentially very large numbers of edges between two vertices, each edge labeled with rich information.
Deep Hashing for Signed Social Network Embedding
Network embedding is a promising way of network representation, facilitating many signed social network processing and analysis tasks such as link prediction and node classification.
The Truly Deep Graph Convolutional Networks for Node Classification
More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper.
Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks
This paper proposes a method to guide tensor factorization, using class labels.
Node Attribute Generation on Graphs
NANG learns a unifying latent representation which is shared by both node attributes and graph structures and can be translated to different modalities.
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling
Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model.
GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION RELATIONAL REASONING
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.
k-hop Graph Neural Networks
We show that the proposed architecture can identify fundamental graph properties.
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN.
GRAPH CLASSIFICATION GRAPH EMBEDDING MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING NODE CLASSIFICATION