A GNN fairness model edits graphs for higher class homophily and lower sensitive-attribute homophily, then trains with supervised contrastive and environmental losses to improve both accuracy and fairness metrics over prior CAF baselines.
Birds of a feather: Homophily in social networks.Annual review of sociology, 27(1):415–444, 2001
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LLM agents in controlled network debates show agreement drift toward specific opinion positions, requiring separation of structural effects from LLM biases before using them as human behavioral proxies.
citing papers explorer
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Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
A GNN fairness model edits graphs for higher class homophily and lower sensitive-attribute homophily, then trains with supervised contrastive and environmental losses to improve both accuracy and fairness metrics over prior CAF baselines.
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Network Effects and Agreement Drift in LLM Debates
LLM agents in controlled network debates show agreement drift toward specific opinion positions, requiring separation of structural effects from LLM biases before using them as human behavioral proxies.