Title: Sampling from Social Networks with Attributes,
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 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.