Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study
arXiv:2605.21510v1 Announce Type: cross Abstract: Reference-based graph compression encodes each vertex's neighbor list relative to a recent vertex, exploiting locality to compress large directed graphs. The dominant tool, WebGraph's BVGraph, fixes a single encoding pipeline and relies on a separately chosen vertex ordering -- typically URL-lexicographic or Layered Label Propagation (LLP). The interaction between ordering and encoder is rarely measured. We propose a two-stage Leiden+LLP vertex ordering -- global LLP to seed labels, Leiden community detection, then per-cluster LLP on each induc
The continuous growth of large graphs, such as social networks and the web, necessitates more efficient compression techniques to manage their scale and computational demands.
Improved graph compression is critical for handling massive AI datasets and enabling more efficient computation and storage, particularly for graph-based machine learning applications.
This research proposes a new vertex ordering method that significantly enhances graph compression, potentially leading to more scalable and performant graph-based AI systems.
- · AI/ML researchers
- · Cloud providers
- · Big data companies
- · Inefficient graph database technologies
More efficient storage and processing of large graph datasets.
Enabling the development of larger and more complex graph neural networks.
Potentially accelerating research in areas like social network analysis, drug discovery, and knowledge graph construction.
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Read at arXiv cs.LG