# PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes

arXiv: https://arxiv.org/abs/2507.20967 · HTML: https://mlsanity.com/paper/2507.20967/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations](https://mlsanity.com/paper/2202.02514/) (2022)
- [Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs](https://mlsanity.com/paper/2307.03393/) (2023)
- [Can Language Models Solve Graph Problems in Natural Language?](https://mlsanity.com/paper/2305.10037/) (2023)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats](https://mlsanity.com/paper/2001.01525/) (2020)
- [A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models](https://mlsanity.com/paper/2208.01230/) (2022)
- [Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance](https://mlsanity.com/paper/2308.05034/) (2023)
- [MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning](https://mlsanity.com/paper/2310.09831/) (2023)
- [GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?](https://mlsanity.com/paper/2310.13833/) (2023)
- [Graph Generative Pre-trained Transformer](https://mlsanity.com/paper/2501.01073/) (2025)
- [SIGL: Securing Software Installations Through Deep Graph Learning](https://mlsanity.com/paper/2008.11533/) (2020)
- [On generating network traffic datasets with synthetic attacks for intrusion detection](https://mlsanity.com/paper/1905.00304/) (2019)
- [Efficient Dynamic Attributed Graph Generation](https://mlsanity.com/paper/2412.08810/) (2024)
- [IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation](https://mlsanity.com/paper/2307.06698/) (2023)

## Built on by (most cited first, 1 of 1)

- [An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation](https://mlsanity.com/paper/2603.17100/) (2026)

## Abstract (arXiv)

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.