Network discovery using content and homophily
| Autor: | Rajmonda S. Caceres, Timothy Greer, Kenneth D. Senne, Molly McMahon, Steven T. Smith |
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| Rok vydání: | 2017 |
| Předmět: |
Theoretical computer science
Network discovery Computer science 02 engineering and technology computer.software_genre Graph Homophily Vertex (geometry) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm design Data mining computer |
| Zdroj: | ICASSP |
| Popis: | A new approach for targeted graph sampling is proposed in which graph sampling and classification occur together, and content-based homophily is exploited to achieve improved classification performance. The application of network discovery of relevant content is considered using an approach that may be generalized to a broad class of vertex properties. The resulting procedure provides the initial step of a graph analytic processing chain whose performance is directly affected by the quality of graph sampling. The performance of the algorithm is measured with real network data and content observed on a social media site. Precision-Recall performance improvements of 30% are demonstrated with this dataset, compared to a baseline approach that does not exploit homophily. Because real-world graphs grow exponentially, this performance improvement may have a significant impact on graph analytic algorithms with sensitivities to the graph sampling quality. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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