A two-stage Leiden+LLP ordering saves 0.3-5.4 bits per edge on poorly ordered graphs across encoders, while new BG/CS/CG encoders improve over BVGraph high-compression by 2-9%.
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Learned static functions combine per-key ML-predicted prefix codes with classic static function storage to compress static key-value mappings beyond zero-order entropy limits.
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Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study
A two-stage Leiden+LLP ordering saves 0.3-5.4 bits per edge on poorly ordered graphs across encoders, while new BG/CS/CG encoders improve over BVGraph high-compression by 2-9%.
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Learned Static Function Data Structures
Learned static functions combine per-key ML-predicted prefix codes with classic static function storage to compress static key-value mappings beyond zero-order entropy limits.