In a reply letter to Adams’ “Distant Friends, Close Strangers? Inferring Friendships from Behavior“, Pentland, Eagle et. al. write:
…We believe that even studies of inherently social phenomena, such as the spread of influence or supposed “social contagions”, can benefit strongly from a focus on objective behavioral data. For instance, the conventional wisdom is that social influence only travels along self-perceived ties. However, in truth, it remains unknown how much is being hidden from us by recency and saliency cognitive filters, and significant social influence, may, in fact, travel across unperceived ties. Behavioral data are not prone to such filters and thus, when used properly, may shed considerable light on such important questions.
When we look at understanding consumer preferences, within the backdrop of social interaction and influence, we can benefit by capturing behavioral data (as oppose to explicit social relationship data). To the extent that we can “infer” relationships between people based on their behaviors, we would need to test whether the relationships inferred are correct. But how?
If the predictions that we make on an individual’s behavior (using inferred social relationships) are correct, than our networks must correctly model reality.
(VisualComplexity.com)
What this means is that the implied social network we extrapolate from user behavior is right if it leads to the right predictions. But this raises important questions:
- Can we really say that the networks are (quasi-)isomorphic?
- Are there (almost infinite) permutations of real networks that can all equally be used for real-world prediction?