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Graph Classification

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Simplifying Graph Convolutional Networks

19 Feb 2019stellargraph/stellargraph

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

GRAPH CLASSIFICATION IMAGE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS TEXT CLASSIFICATION

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

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

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

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

Kernel Graph Convolutional Neural Networks

29 Oct 2017giannisnik/cnn-graph-classification

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

GRAPH CLASSIFICATION

Residual Gated Graph ConvNets

ICLR 2018 xbresson/spatial_graph_convnets

In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.

GRAPH CLASSIFICATION GRAPH CLUSTERING