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Graph classification receives a great deal of attention from the non-Euclidean machine learning community. Recent advances in graph coarsening ...

A graph classification dataset usually contains two types of elements: a set of graphs, and their graph-level labels. Similar to an image classification ...

30.10.2020 · Benchmark dataset for graph classification. This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks. The purpose of this dataset is to make the features on …

23.06.2018 · A Repository of Benchmark Graph Datasets for Graph Classification Introduction to Graph Classification. Recent years have witnessed an increasing number of applications involving objects with structural relationships, including chemical compounds in Bioinformatics, brain networks, image structures, and academic citation networks.

Simple Graph Classification Task

[2] Semi-supervised Classification with Graph Convolutional Networks, ... The dataset includes 188 graphs with 18 nodes and 20 edges on average for each ...

Download scientific diagram | Summary of statistics of the graph classification datasets Dataset samples classes avg. nodes avg. edges node attr. node ...

Graph Classification. 210 papers with code • 54 benchmarks • 31 datasets. ( Image credit: Hierarchical Graph Pooling with Structure Learning )

The NCI graph datasets are commonly used as the benchmark for graph classification. Each NCI dataset belongs to a bioassay task for anticancer activity ...

Jun 23, 2018 · The NCI graph datasets are commonly used as the benchmark for graph classification. Each NCI dataset belongs to a bioassay task for anticancer activity prediction, where each chemical compound is represented as a graph, with atoms representing nodes and bonds as edges.

Each item in the graph classification dataset is a pair of a graph and its label. One can speed up the data loading process by taking advantage of the GraphDataLoader to iterate over the dataset of graphs in mini-batches.

55 rader · Graph Classification. 210 papers with code • 54 benchmarks • 31 datasets. ( Image …

LDP + distance. 74.7%. A simple yet effective baseline for non-attributed graph classification. 2018. 50. δ-2-LWL. 74.60%. Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings.

Boosting-GNN uses GCN, GAT, and GraphSAGE as base classifiers, improving the classification accuracy on imbalanced datasets.

Oct 30, 2020 · Benchmark dataset for graph classification This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks. The purpose of this dataset is to make the features on the nodes and the adjacency matrix to be completely uninformative if considered alone.

The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are ...

12.12.2019 · Graph Classification Data Sets. This repo contains manually curated list of graph datasets for evaluation graph classification methods. These data sets are results of removing isomorphic copies of graphs from the original data sets. There are at the moment 54 data sets.

A collection of benchmark datasets for graph classification and regression. ... This page contains collected benchmark datasets for the evaluation of graph ...