Size Matters: Large Graph Generation with HiGGs
Large graphs are present in a variety of domains, including social networks, civil infrastructure, and the physical sciences to name a few. Graph generation is similarly widespread, with applications in drug discovery, network analysis and synthetic datasets among others. While GNN (Graph Neural Net...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
20.06.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Large graphs are present in a variety of domains, including social networks,
civil infrastructure, and the physical sciences to name a few. Graph generation
is similarly widespread, with applications in drug discovery, network analysis
and synthetic datasets among others. While GNN (Graph Neural Network) models
have been applied in these domains their high in-memory costs restrict them to
small graphs. Conversely less costly rule-based methods struggle to reproduce
complex structures. We propose HIGGS (Hierarchical Generation of Graphs) as a
model-agnostic framework of producing large graphs with realistic local
structures. HIGGS uses GNN models with conditional generation capabilities to
sample graphs in hierarchies of resolution. As a result HIGGS has the capacity
to extend the scale of generated graphs from a given GNN model by quadratic
order. As a demonstration we implement HIGGS using DiGress, a recent
graph-diffusion model, including a novel edge-predictive-diffusion variant
edge-DiGress. We use this implementation to generate categorically attributed
graphs with tens of thousands of nodes. These HIGGS generated graphs are far
larger than any previously produced using GNNs. Despite this jump in scale we
demonstrate that the graphs produced by HIGGS are, on the local scale, more
realistic than those from the rule-based model BTER. |
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DOI: | 10.48550/arxiv.2306.11412 |