방법 비교

	지수 무작위 그래프 모형 (ERGM / p*)	그래프 신경망
분야	네트워크 분석	딥러닝
계열	Process / pipeline	Machine learning
기원 연도	1986 (foundational); modern ERGM framework 1996–2007	2017
창시자	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Kipf, T.N. & Welling, M.
유형	Probabilistic generative network model	Deep learning on graph-structured data
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.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.
별칭	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional 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.	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.