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This framework combines Multi-scale Time Encoding, which captures record age using learnable time scales and type-specific projections, with Rotary Time Encoding, which represents signed inter-record intervals through rotary transformations during graph propagation.
Gains in VisKG-LM show that the complete visual-memory interface adds value beyond path textualization alone and support compiled visual memory as an alternative to online graph propagation.
THGFM is proposed, a web-scale temporal heterogeneous graph fusion model that addresses both limitations within a unified dual-path architecture and directly incorporates relative time into the attention score.
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