Towards Quantifying Sampling Bias in Network Inference

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

Research output: Contribution to Book/Report typesConference contributionpeer-review

Abstract (may include machine translation)

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 strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. This finding suggests that the impact of network structure on relational classification is more complex than previously thought.

Original languageEnglish
Title of host publicationThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages1277-1285
Number of pages9
ISBN (Electronic)9781450356404
DOIs
StatePublished - 23 Apr 2018
Externally publishedYes
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: 23 Apr 201827 Apr 2018

Publication series

NameThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
Country/TerritoryFrance
CityLyon
Period23/04/1827/04/18

Keywords

  • bias
  • relational classification
  • sampling networks

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