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| Centralitás-elemzés× | Hálózatágyazás× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1979 | 2014 (DeepWalk); 2016 (Node2Vec) |
| Megalkotó≠ | Linton C. Freeman | — |
| Típus≠ | Descriptive / exploratory network measure family | Representation learning / unsupervised network method |
| Alapmű≠ | Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. 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 ↗ |
| Alternatív nevek | Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality | node embedding, graph embedding, network representation learning, Ağ Gömme (Node2Vec, DeepWalk, LINE) |
| Kapcsolódó≠ | 5 | 3 |
| Összefoglaló≠ | Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors. | 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. |
| ScholarGateAdatkészlet ↗ |
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