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Greatest papers with code

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

21 Apr 2019google-research/google-research

Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.

FEATURE ENGINEERING GRAPH CLASSIFICATION GRAPH SIMILARITY

Principal Neighbourhood Aggregation for Graph Nets

NeurIPS 2020 rusty1s/pytorch_geometric

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION

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

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

CVPR 2018 rusty1s/pytorch_geometric

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.

GRAPH CLASSIFICATION NODE CLASSIFICATION SUPERPIXEL IMAGE CLASSIFICATION

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

6 Feb 2020microsoft/recommenders

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

GRAPH CLASSIFICATION RECOMMENDATION SYSTEMS

Semi-Supervised Classification with Graph Convolutional Networks

9 Sep 2016tkipf/gcn

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.

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Graph Attention Networks

ICLR 2018 aymericdamien/TopDeepLearning

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.

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Inductive Representation Learning on Large Graphs

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.

GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION REPRESENTATION LEARNING

Structural Deep Network Embedding

KDD 2016 shenweichen/GraphEmbedding

Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.

GRAPH CLASSIFICATION LINK PREDICTION NETWORK EMBEDDING

Graph Neural Networks in TensorFlow and Keras with Spektral

22 Jun 2020danielegrattarola/spektral

In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface.

GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION