# Novel data structures for label based queries specifically efficient for billion+ property graph networks using Kinetica-Graph

arXiv: https://arxiv.org/abs/2311.03631 · HTML: https://mlsanity.com/paper/2311.03631/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [An Ad-hoc graph node vector embedding algorithm for general knowledge graphs using Kinetica-Graph](https://mlsanity.com/paper/2407.15906/) (2024)
- [Hidden relationships in a document-derived property graph: top-k chunk embeddings and inverse-distance weighting over a dynamically evolving ontology](https://mlsanity.com/paper/2609.00387/) (2026)

## Abstract (arXiv)

This paper discusses a novel data structure that efficiently implements label based graph queries particularly for very large graphs. The major issues in large graph databases is the memory foot-print of label based property associations to graph entities and subsequent query speeds. To this end, unlike the available graph databases, that use key-value pairs using map like associative containers, we have devised a novel data structure that is superior in its memory foot-print as well as its fast search characteristics without any compromise on the number of labels that can be associated to graph nodes and edges. We will demonstrate the power of this novel unconventional data structure over billion plus graphs within the context.