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arXiv · 2310.02859

Tight Sampling in Unbounded Networks

Kshitijaa Jaglan·Meher Chaitanya·Triansh Sharma·Abhijeeth Singam·Nidhi Goyal·Ponnurangam Kumaraguru·Ulrik Brandes
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Abstract

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.

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Kshitijaa Jaglan, Meher Chaitanya, Triansh Sharma, Abhijeeth Singam, Nidhi Goyal, Ponnurangam Kumaraguru, Ulrik Brandes. 2023-10-04. Tight Sampling in Unbounded Networks. https://arxiv.org/abs/2310.02859

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