Tight Sampling in Unbounded Networks
| Autor: | Jaglan, Kshitijaa, Chaitanya, Meher, Sharma, Triansh, Singam, Abhijeeth, Goyal, Nidhi, Kumaraguru, Ponnurangam, Brandes, Ulrik |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to prioritize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired. Comment: The first two authors contributed equally |
| Databáze: | arXiv |
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