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Explaining classification performance and bias via network structure and sampling technique
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
Collective inference
Input bias
Network structure
Output bias
Relational classification
Sampling bias
Social networks
Multidisciplinary
Computer Networks and Communications
Computational Mathematics
Input bias
Network structure
Output bias
Relational classification
Sampling bias
Social networks
Multidisciplinary
Computer Networks and Communications
Computational Mathematics
URI
https://hdl.handle.net/20.500.14018/27369
Abstract
Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision-making to scientific research to infer missing attributes using machine learning. However, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the network and the training sample influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imperfect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground-truth is available.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2021-12
Language
ISBN
Identifiers
10.1007/s41109-021-00394-3