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Sampling from Social Networks with Attributes
[conference paper]
Abstract Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the spec... view more
Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.... view less
Keywords
gender relations; sample; sampling error; measurement; ranking; interaction pattern; facebook; statistical analysis; random sample; selection procedure; group size; social media; data; twitter; social network; gender-specific factors; data capture
Classification
Natural Science and Engineering, Applied Sciences
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
social networks; sampling methods; sampling bias; homophily; Homophobie
Collection Title
Proceedings of the 26th International Conference on World Wide Web 2017
Conference
26. International Conference on World Wide Web (WWW'17). Perth, 2017
Document language
English
Publication Year
2017
Publisher
ACM
Page/Pages
p. 1181-1190
DOI
https://doi.org/10.1145/3038912.3052665
ISBN
978-1-4503-4913-0
Status
Published Version; peer reviewed
Licence
Deposit Licence - No Redistribution, No Modifications