Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Community Detection× | Netwerk-embedding× | |
|---|---|---|
| Vakgebied | Netwerkanalyse | Netwerkanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2002–2019 (algorithm family) | 2014 (DeepWalk); 2016 (Node2Vec) |
| Grondlegger≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | — |
| Type≠ | Graph-partitioning / clustering algorithm family | Representation learning / unsupervised network method |
| Oorspronkelijke bron≠ | 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. DOI ↗ | Grover, A. & Leskovec, J. (2016). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 855-864. DOI ↗ |
| Aliassen≠ | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | node embedding, graph embedding, network representation learning, Ağ Gömme (Node2Vec, DeepWalk, LINE) |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | 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? | Network embedding is a family of representation-learning methods that map each node of a graph into a dense, low-dimensional vector while preserving the network's structural properties. The approach was formalised for social-network data by Perozzi, Al-Rfou, and Skiena with DeepWalk (2014), which adapted the Word2Vec skip-gram model to random walks on graphs, and extended by Grover and Leskovec with Node2Vec (2016), which introduced a biased random walk that balances breadth-first and depth-first exploration. These embeddings turn relational data into feature vectors that standard machine-learning classifiers and clustering algorithms can consume directly. |
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