PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes

• URL: http://arxiv.org/abs/2507.20967v1
• Date: Mon, 28 Jul 2025 16:22:50 GMT
• Title: PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes
• Authors: Tianhao Wang, Simon Klancher, Kunal Mukherjee, Josh Wiedemeier, Feng Chen, Murat Kantarcioglu, Kangkook Jee,
• Abstract summary: We introduce ProvCreator, a synthetic graph framework for complex heterogeneous graphs.<n>ProvCreator formulates graph synthesis as a sequence generation task.<n>It features a versatile graph-to-sequence encoder-decoder that supports end-to-end, learnable graph generation.
• Score: 14.078355036170155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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