17 papers with code ·
Graphs

Subtask of
Representation Learning

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We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

#2 best model for Graph Classification on REDDIT-B

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

ICLR 2019 • weihua916/powerful-gnns •

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

SOTA for Graph Classification on REDDIT-B

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 Wikipedia

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

NeurIPS 2018 • RexYing/diffpool

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

vuptran/graph-representation-learning •

•We examine two fundamental tasks associated with graph representation learning: link prediction and node classification.

#2 best model for Link Prediction on Citeseer (Accuracy metric)

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

vuptran/graph-representation-learning •

•We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification.

#11 best model for Node Classification on Pubmed

GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

Capturing such evolution is key to predicting the properties of unseen networks.

EMNLP 2018 • OceanskySun/GraftNet

In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.

GRAPH REPRESENTATION LEARNING OPEN-DOMAIN QUESTION ANSWERING

NeurIPS 2018 • dalab/hyperbolic_nn •

However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.

GRAPH REPRESENTATION LEARNING NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDINGS

NeurIPS 2018 • huangwb/AS-GCN •

Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices.

SOTA for Node Classification on Cora (using extra training data)