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| Multilagsnetværksanalyse× | Netværksindlejring× | |
|---|---|---|
| Fagområde | Netværksanalyse | Netværksanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2013–2014 (formal mathematical framework) | 2014 (DeepWalk); 2016 (Node2Vec) |
| Ophavsperson≠ | Kivelä et al. (2014); De Domenico et al. (2013) | — |
| Type≠ | Graph-theoretic network model | Representation learning / unsupervised network method |
| Oprindelig kilde≠ | Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271. 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 ↗ |
| Aliasser≠ | multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks) | node embedding, graph embedding, network representation learning, Ağ Gömme (Node2Vec, DeepWalk, LINE) |
| Relaterede≠ | 6 | 3 |
| Resumé≠ | Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges. | 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. |
| ScholarGateDatasæt ↗ |
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