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A Simple Baseline Algorithm for Graph Classification

2018-10-22Code Available0· sign in to hype

Nathan de Lara, Edouard Pineau

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Abstract

Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and require heavy computational power to achieve their best performance. We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method obtains competitive results compared to state-of-the-art algorithms.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
D&DSF + RFCAccuracy24.6Unverified
ENZYMESSF + RFCAccuracy43.7Unverified
MUTAGSF + RFCAccuracy88.4Unverified
NCI1SF + RFCAccuracy75.2Unverified
PROTEINSSF + RFCAccuracy73.6Unverified
PTCSF + RFCAccuracy62.8Unverified

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