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Evolving interaction graphs can obscure peer effects, but DynaNet-DR reveals how to disentangle these complexities for more accurate causal estimates.
Transporting causal effects across different network populations can significantly enhance intervention strategies in social networks and public health.
By actively exploring knowledge graphs with a differentiable neural-symbolic approach, NeuroSymActive achieves strong KGQA accuracy while drastically reducing the computational cost of graph lookups.
