방법 비교

	지수 무작위 그래프 모형 (ERGM / p*)	그래프 어텐션 네트워크
분야	네트워크 분석	딥러닝
계열	Process / pipeline	Machine learning
기원 연도	1986 (foundational); modern ERGM framework 1996–2007	2018
창시자	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Veličković, P. et al.
유형	Probabilistic generative network model	Graph neural network (attention-based)
원전	Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
별칭	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
관련	6	4
요약	The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes.	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).