TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs

TL;DR

TeraHAC is a scalable, approximate hierarchical clustering algorithm designed for trillion-edge graphs, significantly reducing computation rounds and time while maintaining high clustering quality.

Contribution

It introduces a novel scalable approach combining nearest-neighbor chains with approximate HAC, enabling efficient clustering of massive graphs across multiple machines.

Findings

Requires over 100x fewer rounds than previous methods

Up to 8.3x faster than the state-of-the-art distributed algorithm

Achieves 1.16x higher clustering quality

Abstract

We introduce TeraHAC, a $(1+\epsilon)$-approximate hierarchical agglomerative clustering (HAC) algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to computing $(1+\epsilon)$-approximate HAC, which is a novel combination of the nearest-neighbor chain algorithm and the notion of $(1+\epsilon)$-approximate HAC. Our approach allows us to partition the graph among multiple machines and make significant progress in computing the clustering within each partition before any communication with other partitions is needed. We evaluate TeraHAC on a number of real-world and synthetic graphs of up to 8 trillion edges. We show that TeraHAC requires over 100x fewer rounds compared to previously known approaches for computing HAC. It is up to 8.3x faster than SCC, the state-of-the-art distributed algorithm for hierarchical clustering, while achieving 1.16x higher quality. In fact, TeraHAC essentially retains the quality of the celebrated HAC algorithm while significantly improving the running time.