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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

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 GRAPH NEURAL NETWORK

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 NEURAL NETWORK GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

CVPR 2017 mys007/ecc

A number of problems can be formulated as prediction on graph-structured data.

GRAPH CLASSIFICATION

GraKeL: A Graph Kernel Library in Python

6 Jun 2018ysig/GraKeL

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.

GRAPH CLASSIFICATION

Graph Classification using Structural Attention

KDD 2018 benedekrozemberczki/GAM

Graph classification is a problem with practical applications in many different domains.

GRAPH CLASSIFICATION

Graph Edit Distance Computation via Graph Neural Networks

16 Aug 2018benedekrozemberczki/SimGNN

Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK GRAPH SIMILARITY

Kernel Graph Convolutional Neural Networks

29 Oct 2017giannisnik/cnn-graph-classification

Graph kernels have been successfully applied to many graph classification problems.

GRAPH CLASSIFICATION