方法对比
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| k-核分解× | PageRank Centrality× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 1983 | 1999 |
| 提出者≠ | Stephen B. Seidman | Page, Brin, Motwani & Winograd |
| 类型≠ | Graph pruning and hierarchical decomposition | Iterative link-based centrality algorithm |
| 开创性文献≠ | Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287. DOI ↗ | Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report. link ↗ |
| 别名 | Core Decomposition, Coreness Decomposition, Shell Decomposition, Çekirdek Ayrıştırma | Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği |
| 相关≠ | 3 | 2 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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