Imaginary network motifs: Structural patterns of false positives and negatives in social networks.

We examine the structural patterns in the cognitive representation of social networks by systematically classifying false positives and negatives. Although existing literature on Cognitive Social Structures (CSS) has begun exploring false positives and negatives by comparing actual and perceived networks, it has not differentiated simultaneous occurrences of true and false positives and negatives on network motifs, such as reciprocity and triadic closure. Here, we propose a theoretical framework to categorize three classes of errors we call imaginary network motifs as combinations of accurately and erroneously perceived ties: (a) partially false, (b) completely false, and (c) mixed false. Using four published CSS data sets, we empirically test which imaginary network motifs are significantly more or less present in different types of perceived networks than the corresponding actual networks. Our results confirm that people not only fill in the blanks as suggested in the prior research but also conceive other imaginary structures. The findings advance our understanding of perception gaps between actual and perceived networks and have implications for designing more accurate network modeling and sampling. • The dyad census approach of a multiplex motif method is developed and applied to Cognitive Social Structure data. • The approach identifies a hidden category of mixed false where false positives and negatives co-occur. • The method reveals false positives and negatives occur with true positives and negatives. • Most false positives and negatives happen in proxy ties rather than the ones people report about themselves. • Perceived network density can partially explain the patterns of false positives and negatives. [ABSTRACT FROM AUTHOR]