手法を比較

	DBSCAN	グラフ注意機構ネットワーク	グラフニューラルネットワーク
分野	機械学習	深層学習	深層学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1996	2018	2017
提唱者	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.	Kipf, T.N. & Welling, M.
種類	Density-based clustering algorithm	Graph neural network (attention-based)	Deep learning on graph-structured data
原典	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.
別名	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network
関連	3	4	4
概要	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).	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.