This doesn't even come close to the power of (not even particularly) modern collaborative filtering algorithms.
CF doesn't need to assume that A talks to B well and B talks to C well implies A talks to C well- rather, it typically tries to learn an embedding for A, B, C that predicts their interactions effectively.
In the simplest form, this could be applying SVD or nonnegative matrix factorization to the user interaction matrix, truncating to get a low-rank symmetric approximation. More advanced forms would try to predict the interaction as some function of the two user embeddings, possibly even a neural network.
This only shows that a particular feature based on 2-chains of interactions isn't useful, whereas the standard approaches I described can try to infer information across any number of connections, and don't necessarily assume that 2-chains as described are indicative of a particular result.
CF doesn't need to assume that A talks to B well and B talks to C well implies A talks to C well- rather, it typically tries to learn an embedding for A, B, C that predicts their interactions effectively.
In the simplest form, this could be applying SVD or nonnegative matrix factorization to the user interaction matrix, truncating to get a low-rank symmetric approximation. More advanced forms would try to predict the interaction as some function of the two user embeddings, possibly even a neural network.
This only shows that a particular feature based on 2-chains of interactions isn't useful, whereas the standard approaches I described can try to infer information across any number of connections, and don't necessarily assume that 2-chains as described are indicative of a particular result.