Kyosuke Tanaka’s new joint work (with George Vega Yon at the University of Utah) at Social Networks explored Cognitive Social Structures (CSS) through a lens that might seem straight out of the imagination. Let's dive into what they've discovered and how it might reshape our understanding of social connections!

Imaginary Network Motifs: Exploring the unseen image patterns comparing the web of relationships people think they have (the perceived network) with what actually exists (the actual network). We've named these comparison patterns "imaginary network motifs." They involve both accurately and inaccurately perceived ties, whether they're real or just in our minds. 

What Did They Find? 

• Uncovering Patterns in CSS Data: Their method, using something called the "dyad census," lets them spot specific patterns in CSS data sets. 
• The Mystery of Mixed Motifs: They uncovered a hidden category where both false positives and negatives coexist. Imagine finding out some connections you thought were there aren't, and vice versa. 
• Errors Galore: Mistakes happen in both partial and complete motifs – meaning, sometimes we get part of a connection wrong, and other times we miss the whole thing. 
• Network Density Surprise: While previous studies hinted at a connection between network density and mistakes, their analysis shows it's not as straightforward as we thought. 
• Proxy Ties Take the Hit: Most errors happen in proxy ties rather than the ones people report about themselves. In other words, the connections we think others have are trickier to get right.

Here's another research on pushing the boundaries, unlocking mysteries, and making sense of the social puzzle! Their research is shaking things up, offering new ways to understand CSS and opening doors for more discoveries in social network analysis.

Note that CSS is a cognitive representation of social networks from each person in the social networks (see Wikipedia entry for more information).

The article is available and open access:
Tanaka, K., & Vega Yon, G. G. (2024). Imaginary network motifs: Structural patterns of false positives and negatives in social networks. Social Networks, 78, 65-80.