Early detection of persistent topics in social networks
Dados Bibliográficos
| ID | 16858905 |
|---|---|
| Autores | Shota Saito (0000-0002-4463-6073, The University of Tokyo, autor correspondente), Ryota Tomioka (0000-0002-8092-6553, Toyota Technological Institute at Chicago), Kenji Yamanishi (0000-0001-7370-9991, Japan Science and Technology Agency) |
| Ano | 2015 |
| Volume | 5 |
| Fascículo | 1 |
| Data de publicação | 2015-12-01 |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Social Network Analysis and Mining (JOURNAL) |
| Identificadores do periódico | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Editora | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-015-0257-1 |
| OpenAlex | W2245740819 |
| Idioma | EN |
| Referências citadas | 32 |
Anomaly detection · Data mining · Graph · Principal component analysis · Subspace topology · Support vector machine · Complex Network Analysis Techniques · Computer Science · Opinion Dynamics and Social Influence · Spam and Phishing Detection · Artificial Intelligence · Theoretical Computer Science
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Measuring User Influence in Twitter
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The Collective Dynamics of Smoking in a Large Social Network
Collective dynamics of ‘small-world’ networks
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Accessibility of cities in the digital economy
Social Network Sites
| Altamente citado | Não |
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