Sammenlign metoder

	Analyse af small-world- og skaleringsfri netværk	Exponential Random Graph Model (ERGM / p*)
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1998 (small-world); 1999 (scale-free)	1986 (foundational); modern ERGM framework 1996–2007
Ophavsperson	—	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)
Type	Descriptive / exploratory network analysis	Probabilistic generative network model
Oprindelig kilde	Watts, D.J. & Strogatz, S.H. (1998). Collective Dynamics of 'Small-World' Networks. Nature, 393(6684), 440-442.	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.
Aliasser	Küçük Dünya ve Ölçek-Bağımsız Ağ Analizi, small-world network, scale-free network, preferential attachment analysis	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)
Relaterede	9	6
Resumé	Small-world and scale-free network analysis tests whether a real-world network exhibits two landmark topological signatures identified in 1998-1999: the Watts-Strogatz small-world property (high local clustering combined with short average path lengths) and the Barabási-Albert scale-free property (a degree distribution that follows a power law, meaning a small number of hubs connect to a disproportionately large share of other nodes). Together these frameworks transformed network science by showing that many social, biological, and technological networks share a common structural grammar.	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.