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Graph Classification

382 papers with code • 65 benchmarks • 46 datasets

Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

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Fake News Detection on Social Media using Geometric Deep Learning

gordicaleksa/pytorch-GAT • • 10 Feb 2019

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

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Fast Graph Representation Learning with PyTorch Geometric

rusty1s/pytorch_geometric • • 6 Mar 2019

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

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Spectral Clustering with Graph Neural Networks for Graph Pooling

FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling • • ICML 2020

Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.

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Composition-based Multi-Relational Graph Convolutional Networks

malllabiisc/CompGCN • • ICLR 2020

Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.

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A Fair Comparison of Graph Neural Networks for Graph Classification

diningphil/gnn-comparison • • ICLR 2020

We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

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Contrastive Multi-View Representation Learning on Graphs

kavehhassani/mvgrl • • ICML 2020

We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol.

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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

THUDM/GCC • • 17 Jun 2020

Graph representation learning has emerged as a powerful technique for addressing real-world problems.

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Simple and Deep Graph Convolutional Networks

chennnM/GCNII • • ICML 2020

We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.

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Detecting Beneficial Feature Interactions for Recommender Systems

ruizhang-ai/SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems • • 2 Aug 2020

To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy.

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Do Transformers Really Perform Bad for Graph Representation?

Microsoft/Graphormer • • 9 Jun 2021

Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.

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