48 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.

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.

SOTA for Node Classification on NELL

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.

#5 best model for Node Classification on Cora

We present a semi-supervised learning framework based on graph embeddings.

#2 best model for Node Classification on NELL

DOCUMENT CLASSIFICATION ENTITY EXTRACTION NODE CLASSIFICATION

NeurIPS 2017 • williamleif/GraphSAGE •

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.

We present DeepWalk, a novel approach for learning latent representations of vertices in a network.

#3 best model for Node Classification on Wikipedia

ANOMALY DETECTION DOCUMENT CLASSIFICATION LANGUAGE MODELLING NODE CLASSIFICATION

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

#5 best model for Node Classification on Wikipedia

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

ICLR 2018 • PetarV-/GAT •

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

#3 best model for Document Classification on Cora

DOCUMENT CLASSIFICATION GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

SeongokRyu/Graph-neural-networks •

•The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

COMMUNITY DETECTION IMAGE CLASSIFICATION MATRIX COMPLETION NODE CLASSIFICATION

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.

SOTA for Node Classification on BlogCatalog

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION