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Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: effectively representing unseen nodes and mitigating noisy or redundant graph info
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