# A Simple Baseline Algorithm for Graph Classification

arXiv: https://arxiv.org/abs/1810.09155 · HTML: https://mlsanity.com/paper/1810.09155/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 15 of 15)

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- [Simplicial Complex Representation Learning](https://mlsanity.com/paper/2103.04046/) (2021)
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- [Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification](https://mlsanity.com/paper/2407.12136/) (2024)
- [MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning](https://mlsanity.com/paper/2206.09280/) (2022)
- [Computing Graph Descriptors on Edge Streams](https://mlsanity.com/paper/2109.01494/) (2021)
- [Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks](https://mlsanity.com/paper/2409.18235/) (2024)
- [TREE-G: Decision Trees Contesting Graph Neural Networks](https://mlsanity.com/paper/2207.02760/) (2022)
- [Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers](https://mlsanity.com/paper/2406.15881/) (2024)
- [Counterfactual Explanations for Graph Classification Through the Lenses of Density](https://mlsanity.com/paper/2307.14849/) (2023)
- [Computationally-efficient Graph Modeling with Refined Graph Random Features](https://mlsanity.com/paper/2510.07716/) (2025)
- [Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks](https://mlsanity.com/paper/2603.04209/) (2026)
- [Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction](https://mlsanity.com/paper/2411.06212/) (2024)

## Abstract (arXiv)

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.