Methoden vergelijken

	k-Core Decomposition	Community Detection	PageRank Centrality
Vakgebied	Netwerkanalyse	Netwerkanalyse	Netwerkanalyse
Familie	Process / pipeline	Process / pipeline	Machine learning
Jaar van ontstaan	1983	2002–2019 (algorithm family)	1999
Grondlegger	Stephen B. Seidman	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Page, Brin, Motwani & Winograd
Type	Graph pruning and hierarchical decomposition	Graph-partitioning / clustering algorithm family	Iterative link-based centrality algorithm
Oorspronkelijke bron	Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.	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.	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.
Aliassen	Core Decomposition, Coreness Decomposition, Shell Decomposition, Çekirdek Ayrıştırma	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği
Verwant	3	5	2
Samenvatting	k-Core Decomposition is a graph-theoretic method that partitions the vertices of a network into a nested sequence of subgraphs called k-cores. A k-core is the maximal subgraph in which every vertex has at least k neighbors within that subgraph. Introduced by Stephen B. Seidman in 1983, the method assigns each vertex a coreness number that captures its structural centrality relative to the local connectivity of the graph.	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?	PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.