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Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

AAAI 2020 · Learning theory and algorithms › Fairness · approach: Fair allocation and selection · 82 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add adversarial pair-level fairness (FLIP) to DeepWalk link predictionDeepWalk · Google+AUC: 0.7708 -> 0.8575Helped
add adversarial pair-level fairness (FLIP) to DeepWalk link predictionDeepWalk · FacebookAUC: 0.951 -> 0.8601Hurt
add adversarial pair-level fairness (FLIP) to DeepWalk link predictionDeepWalk · Dutch schoolmodred: -0.0471 -> 0.3592Helped
apply greedy modularity post-processing (3% flips) to JaccardJaccard · Facebookmodred: -0.1494 -> 0.6613Helped
apply greedy modularity post-processing (3% flips) to JaccardJaccard · Google+AUC: 0.7932 -> 0.7399Hurt
apply equalized-odds post-processing (PEO) to DeepWalkDeepWalk · Dutch schoolAUC: 0.5287 -> 0.4908Hurt
apply equalized-odds post-processing (PEO) to baselinesJac, Ad-Ad, Pr-At, DW · Facebookmodred: Jac -0.1494 -> -0.5575; Ad-Ad 0.2224 -> 0.0132Mixed
apply equalized-odds post-processing (PEO) to JaccardJaccard · Google+modred: 0.0932 -> 3.4696Helped

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