Linganisha mbinu

	Muundo wa Mitandao ya Kielektroniki kwa Kina (ERGM / p*)	DBSCAN
Nyanja	Uchanganuzi wa Mitandao	Ujifunzaji wa Mashine
Familia	Process / pipeline	Machine learning
Mwaka wa asili	1986 (foundational); modern ERGM framework 1996–2007	1996
Mwanzilishi	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Aina	Probabilistic generative network model	Density-based clustering algorithm
Chanzo asilia	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.	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.
Majina mbadala	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Zinazohusiana	6	3
Muhtasari	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.	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.