PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes

TL;DR

ProvCreator introduces a transformer-based framework for generating complex, heterogeneous graphs with high-dimensional attributes, enabling realistic and privacy-aware synthetic data for challenging real-world applications.

Contribution

It presents a novel graph synthesis method using sequence modeling and transformer architectures for complex heterogeneous graphs with rich attributes.

Findings

Successfully models intricate dependencies in system provenance and knowledge graphs.

Generates realistic, privacy-aware synthetic datasets.

Outperforms existing methods in capturing structure and semantics.

Abstract

The rise of graph-structured data has driven interest in graph learning and synthetic data generation. While successful in text and image domains, synthetic graph generation remains challenging -- especially for real-world graphs with complex, heterogeneous schemas. Existing research has focused mostly on homogeneous structures with simple attributes, limiting their usefulness and relevance for application domains requiring semantic fidelity. In this research, we introduce ProvCreator, a synthetic graph framework designed for complex heterogeneous graphs with high-dimensional node and edge attributes. ProvCreator formulates graph synthesis as a sequence generation task, enabling the use of transformer-based large language models. It features a versatile graph-to-sequence encoder-decoder that 1. losslessly encodes graph structure and attributes, 2. efficiently compresses large graphs for contextual modeling, and 3. supports end-to-end, learnable graph generation. To validate our research, we evaluate ProvCreator on two challenging domains: system provenance graphs in cybersecurity and knowledge graphs from IntelliGraph Benchmark Dataset. In both cases, ProvCreator captures intricate dependencies between structure and semantics, enabling the generation of realistic and privacy-aware synthetic datasets.