Σύγκριση μεθόδων

	Ανίχνευση Κοινοτήτων	Μοντέλο Εκθετικών Τυχαίων Γραφημάτων (ERGM / p*)	Μοντέλα Διάχυσης Δικτύων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	2002–2019 (algorithm family)	1986 (foundational); modern ERGM framework 1996–2007	1927 (epidemiological compartmental); 2003 (social influence cascade)
Δημιουργός	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)
Τύπος	Graph-partitioning / clustering algorithm family	Probabilistic generative network model	Stochastic / deterministic simulation on graphs
Θεμελιώδης πηγή	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.	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.	Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721.
Εναλλακτικές ονομασίες	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade)
Συναφείς	5	6	5
Σύνοψη	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 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.	Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time.