awesome graph classification

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Quick description about awesome graph classification:

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

Programming Language: Python.

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