So sánh phương pháp

	Mạng nơ-ron đồ thị	Rừng ngẫu nhiên	Máy Vectơ Hỗ trợ (Phân loại)	XGBoost
Lĩnh vực	Học sâu	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning	Machine learning
Năm ra đời	2017	2001	1995	2016
Người khởi xướng	Kipf, T.N. & Welling, M.	Breiman, L.	Cortes, C. & Vapnik, V.	Chen, T. & Guestrin, C.
Loại	Deep learning on graph-structured data	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)	Ensemble (gradient-boosted decision trees)
Công trình gốc	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Tên gọi khác	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	4	4	5	5
Tóm tắt	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.