Sampling from Social Networks with Attributes

Conference paper | Konferenzbeitrag

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.

Object Details

• ISBN: 978-1-4503-4913-0
• Extent: Seite(n): 1181-1190
• Language: Englisch
• Notes: Status: Veröffentlichungsversion; begutachtet (peer reviewed) ; 26. International Conference on World Wide Web (WWW'17). Perth, 2017

References and Relationships

• Bibliographic citation: Proceedings of the 26th International Conference on World Wide Web 2017

Classification and Topics

• Subject: Naturwissenschaften ; Sozialwissenschaften, Soziologie ; Naturwissenschaften, Technik(wissenschaften), angewandte Wissenschaften ; Erhebungstechniken und Analysetechniken der Sozialwissenschaften ; statistische Analyse ; Datengewinnung ; soziales Netzwerk ; Interaktionsmuster ; Twitter ; Soziale Medien ; Stichprobe ; Messung ; Daten ; Facebook ; Stichprobenfehler ; Ranking ; geschlechtsspezifische Faktoren ; Zufallsauswahl ; Geschlechterverhältnis ; Selektionsverfahren ; Gruppengröße

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Wagner, Claudia ; Singer, Philipp ; Karimi, Fariba ; Pfeffer, Jürgen ; Strohmaier, Markus
• Event: Veröffentlichung
• (who): ACM
• (where): Vereinigte Staaten von Amerika
• (when): 2017

Further information

• DOI: https://doi.org/10.1145/3038912.3052665
• URN: urn:nbn:de:0168-ssoar-66082-2
• Rights: GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
• Last update: 21.06.2024, 4:27 PM CEST