Linganisha mbinu

	Muundo wa Mitandao ya Kielektroniki kwa Kina (ERGM / p*)	Ugunduzi wa Jumuiya	Mtandao wa Makini wa Grafu
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao	Ujifunzaji wa Kina
Familia	Process / pipeline	Process / pipeline	Machine learning
Mwaka wa asili	1986 (foundational); modern ERGM framework 1996–2007	2002–2019 (algorithm family)	2018
Mwanzilishi	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Veličković, P. et al.
Aina	Probabilistic generative network model	Graph-partitioning / clustering algorithm family	Graph neural network (attention-based)
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.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Majina mbadala	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Zinazohusiana	6	5	4
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.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?	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).