HDLouvain: High-Throughput Incremental Clustering for Hyperdimensional Data
Modern shotgun proteomics generates tens of thousands of tandem mass spectra per hour, producing hundreds of gigabytes of data that strain conventional clustering pipelines. DBSCAN and hierarchical agglomerative clustering (HAC) offer flexible similarity search, but DBSCAN is unstable across varying data densities, HAC costs $O(n^2)$, and both yield poor cluster quality. Graph-based methods such as Louvain produce high-quality clusters but scale poorly on streaming data. We observe that Louvain's poor scalability can be resolved without sacrificing its quality, and present HDLouvain, which combines Louvain community detection with the bit-parallel similarity computation of hyperdimensional computing (HDC). HDLouvain partitions a bootstrap fraction of the stream into precursor-mass buckets, applies Louvain to each bucket's kNN similarity graph, and condenses each community into a single binary hypervector by bitwise-majority bundling rather than iterative centroid recomputation. New spectra are assigned by a GPU-accelerated Hamming distance lookup against representatives in their mass bucket, or seed a new cluster when no match meets a similarity threshold $τ$, so the partition grows incrementally without full re-clustering. On five PRIDE datasets (0.99M to 21.13M spectra), sweeping the bootstrap fraction $α$ from 0.2 to 0.9 exposes an explicit, monotonic throughput-fidelity trade-off. On PXD003258, speedup ranges from 4.5x at $α=0.3$ to 1.65x at $α=0.7$. Modularity rises from 0.75-0.76 at $α=0.2$ to 0.95-0.97 at $α=0.9$, never more than 8% below full static recomputation. The Louvain stage achieves 0.86-0.93 normalized mutual information (NMI) on LFR benchmark networks with 30k to 2M nodes, whereas DBSCAN and HAC degrade to 0.57.