পদ্ধতির তুলনা করুন

	Exponential Random Graph Model (ERGM / p*)	গ্রাফ অ্যাটেনশন নেটওয়ার্ক
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	গভীর শিখন
পরিবার	Process / pipeline	Machine learning
উদ্ভবের বছর	1986 (foundational); modern ERGM framework 1996–2007	2018
প্রবর্তক	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Veličković, P. et al.
ধরন	Probabilistic generative network model	Graph neural network (attention-based)
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	6	4
সারসংক্ষেপ	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).