# Towards a property graph generator for benchmarking

## TL;DR

This paper presents DataSynth, a flexible framework for generating customizable property graphs that can model complex property-structure correlations, aiding benchmark designers in efficient large-scale graph creation.

## Contribution

Introduction of DataSynth, a novel property graph generator with customizable schemas and correlation modeling, reducing the need for ad-hoc generator development.

## Key findings

- Preliminary promising results for the property-to-node matching algorithm
- Framework supports customizable schemas and characteristics
- Models correlation between properties and graph structure

## Abstract

The use of synthetic graph generators is a common practice among graph-oriented benchmark designers, as it allows obtaining graphs with the required scale and characteristics. However, finding a graph generator that accurately fits the needs of a given benchmark is very difficult, thus practitioners end up creating ad-hoc ones. Such a task is usually time-consuming, and often leads to reinventing the wheel. In this paper, we introduce the conceptual design of DataSynth, a framework for property graphs generation with customizable schemas and characteristics. The goal of DataSynth is to assist benchmark designers in generating graphs efficiently and at scale, saving from implementing their own generators. Additionally, DataSynth introduces novel features barely explored so far, such as modeling the correlation between properties and the structure of the graph. This is achieved by a novel property-to-node matching algorithm for which we present preliminary promising results.