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Graph Representation Learning

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Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019rusty1s/pytorch_geometric

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

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

22 Nov 2017hwwang55/GraphGAN

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

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

How Powerful are Graph Neural Networks?

ICLR 2019 weihua916/powerful-gnns

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

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING

Hierarchical Graph Representation Learning with Differentiable Pooling

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

Multi-Task Graph Autoencoders

7 Nov 2018vuptran/graph-representation-learning

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

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

Learning to Make Predictions on Graphs with Autoencoders

23 Feb 2018vuptran/graph-representation-learning

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

GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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

Hyperbolic Neural Networks

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

Adaptive Sampling Towards Fast Graph Representation Learning

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)

GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION