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A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.
Features
- Explainable Classification of Brain Networks via Contrast Subgraphs
- A Simple Yet Effective Baseline for Non-Attribute Graph Classification
- Multi-Graph Multi-Label Learning Based on Entropy
- Joint Structure Feature Exploration
- A Scalable Approach to Size-Independent Network Similarity
- Regularization for Multi-Task Graph Classification
Project Samples
License
Creative Commons Attribution LicenseFollow Awesome Graph Classification
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