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Graph Classification

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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

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

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

Modeling Relational Data with Graph Convolutional Networks

17 Mar 2017tkipf/gae

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

Gated Graph Sequence Neural Networks

17 Nov 2015Microsoft/gated-graph-neural-network-samples

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

DRUG DISCOVERY GRAPH CLASSIFICATION NODE CLASSIFICATION SQL-TO-TEXT

Capsule Graph Neural Network

ICLR 2019 benedekrozemberczki/CapsGNN

The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.

GRAPH CLASSIFICATION

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

graph2vec: Learning Distributed Representations of Graphs

17 Jul 2017benedekrozemberczki/graph2vec

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MATCHING

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 REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION