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Link prediction models amplify structural biases beyond immediate connections, but a new $k$-hop fairness approach can mitigate these disparities with favorable performance trade-offs.
Algorithmic recommendations subtly warp network evolution, and this new measure reveals how link prediction strategies amplify existing biases in dynamic graphs.
