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Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Dynamic link prediction is essential for a wide range of domains, including social networks, bioinformatics, knowledge bases, and recommender systems.