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Datasets

Greatest papers with code

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

5 Feb 2020cynricfu/MAGNN

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

Simple and Effective Graph Autoencoders with One-Hop Linear Models

21 Jan 2020deezer/linear_graph_autoencoders

Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.

LINK PREDICTION NODE CLUSTERING

Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks

2 Oct 2019deezer/linear_graph_autoencoders

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.

LINK PREDICTION NODE CLUSTERING

StructPool: Structured Graph Pooling via Conditional Random Fields

ICLR 2020 Nate1874/StructPool

Learning high-level representations for graphs is of great importance for graph analysis tasks.

NODE CLUSTERING STRUCTURED PREDICTION

Heterogeneous Deep Graph Infomax

19 Nov 2019YuxiangRen/Heterogeneous-Deep-Graph-Infomax

The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.

CLASSIFICATION GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION NODE CLUSTERING

RWR-GAE: Random Walk Regularization for Graph Auto Encoders

12 Aug 2019MysteryVaibhav/DW-GAE

Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space.

GRAPH CLUSTERING LINK PREDICTION NODE CLUSTERING

Adaptive Graph Encoder for Attributed Graph Embedding

3 Jul 2020thunlp/AGE

Experimental results show that AGE consistently outperforms state-of-the-art graph embedding methods considerably on these tasks.

GRAPH EMBEDDING LINK PREDICTION NODE CLUSTERING

Simple Spectral Graph Convolution

ICLR 2021 allenhaozhu/SSGC

Our spectral analysis shows that our simple spectral graph convolution used in S^2GC is a low-pass filter which partitions networks into a few large parts.

NODE CLASSIFICATION NODE CLUSTERING TEXT CLASSIFICATION

Accurate Learning of Graph Representations with Graph Multiset Pooling

ICLR 2021 JinheonBaek/GMT

Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.

GRAPH CLASSIFICATION GRAPH CLUSTERING GRAPH EMBEDDING GRAPH GENERATION GRAPH LEARNING GRAPH RECONSTRUCTION NODE CLUSTERING