Tight Sampling in Unbounded Networks


  • Kshitijaa Jaglan IIIT Hyderabad
  • Meher Chaitanya Pindiprolu ETH Zürich
  • Triansh Sharma IIIT Hyderabad
  • Abhijeeth Reddy Singam IIIT Hyderabad
  • Nidhi Goyal IIIT Delhi
  • Ponnurangam Kumaraguru IIIT Hyderabad
  • Ulrik Brandes ETH Zürich




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 the 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.




How to Cite

Jaglan, K., Pindiprolu, M. C., Sharma, T., Singam, A. R., Goyal, N., Kumaraguru, P., & Brandes, U. (2024). Tight Sampling in Unbounded Networks. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 704-716. https://doi.org/10.1609/icwsm.v18i1.31345