Abstract

Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labeled sample How should the network be sampled, and what effect does it have on inference error How does the structure of the network impact the sampling strategy We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labeled, regardless of the sampling …

Date

April 23, 2018

Authors

Lisette Espín-Noboa, Claudia Wagner, Fariba Karimi, Kristina Lerman

Book

Companion Proceedings of the The Web Conference 2018

Pages

1277-1285