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TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs

Summary: TeraHAC achieves (1+ε)-approximate HAC for trillion-edge graphs by fusing nearest-neighbor chain with (1+ε)-HAC, enabling partitioned, communication-efficient clustering. Scales to 8T edges; >100x fewer rounds than prior HAC, up to 8.3x faster than SCC, and preserves HAC quality. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6724
Venue
SIGMOD
Year
2023
Pagerank
4.3047774e-05
Overall Rank
9,681 | 32.66%
DOI
10.1145/3617341

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