CID-TKG combines historical invariance and evolutionary dynamics graphs with contrastive alignment of view-specific relation representations to reach state-of-the-art performance on temporal knowledge graph extrapolation.
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HIM learns hyperbolic user representations from propagation data to estimate influence strength and select seeds for model-agnostic influence maximization.
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CID-TKG: Collaborative Historical Invariance and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning
CID-TKG combines historical invariance and evolutionary dynamics graphs with contrastive alignment of view-specific relation representations to reach state-of-the-art performance on temporal knowledge graph extrapolation.
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Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization
HIM learns hyperbolic user representations from propagation data to estimate influence strength and select seeds for model-agnostic influence maximization.