mlsanityData to 7 Oct 2026

A Simple Baseline Algorithm for Graph Classification

arXiv 1810.09155 ↗First posted 22 Oct 2018cs.LG stat.MLcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 2; 2024: 4; 2025: 1; 2026: 1.

15 papers.

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  1. 2021The Shapley Value of Classifiers in Ensemble Gamescited by 23
  2. 2021Simplicial Complex Representation Learningcited by 17
  3. 2021Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterizationcited by 10
  4. 2021Identity Inference on Blockchain using Graph Neural Networkcited by 9
  5. 2023Efficient Graph Field Integrators Meet Point Cloudscited by 7
  6. 2024Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classificationcited by 7
  7. 2022MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learningcited by 5
  8. 2021Computing Graph Descriptors on Edge Streamscited by 4
  9. 2024Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networkscited by 4
  10. 2022TREE-G: Decision Trees Contesting Graph Neural Networkscited by 3
  11. 2024Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformerscited by 3
  12. 2023Counterfactual Explanations for Graph Classification Through the Lenses of Densitycited by 2
3 more
  1. 2025Computationally-efficient Graph Modeling with Refined Graph Random Featurescited by 2
  2. 2026Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networkscited by 1
  3. 2024Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extractioncited by 0