Astro Generative Network: A Variational Framework for Controlled Node Insertion in Incomplete Complex Networks

2026-05-10

AI Summary

Real-world networks often suffer from missing nodes due to sampling limitations, privacy constraints, or temporal gaps, and existing methods struggle to incorporate new nodes while preserving the original structural properties. This work proposes a controllable node insertion framework for incomplete networks: it leverages a variational graph autoencoder to generate features for new nodes and employs a similarity-driven mechanism to connect them to the observed subgraph, with flexible configuration on whether edges among inserted nodes are permitted. We also introduce the first evaluation framework tailored to this task. Experiments on three synthetic network types demonstrate that our method effectively mitigates artificial cluster inflation, better preserves degree distribution and average path length, and ensures non-trivial separability between original and inserted nodes, significantly outperforming baseline approaches.

Abstract

Empirical networked systems are often only partially observed: sampling frames, crawling policies, privacy constraints, and temporal gaps can leave actors and edges unobserved. This complicates robustness and sensitivity analysis because many graph-learning pipelines implicitly treat the observed node set as exhaustive. Link prediction and graph completion repair structure among known vertices, whereas full-graph generators synthesize new graphs rather than extending an observed one as a fixed backbone. We study the complementary task of controlled node insertion: generating plausible new actors and attaching them to an existing graph while preserving interpretable global topology. We introduce the Astro Generative Network (AGN), a variational graph autoencoder that samples latent vectors to decode node features and then integrates new vertices through similarity-based attachment to the observed backbone. We distinguish the recommended configuration, AGN, from AGN-original, a diagnostic baseline that permits generated-generated edges. Across three synthetic regimes, AGN-original forms dense generated-generated subgraphs that artificially inflate clustering and density. Disabling those edges removes this artifact while preserving degree and path-length behavior. In our experiments, AGN keeps clustering and modularity changes modest relative to pre-insertion values, while novelty diagnostics show non-trivial separation from existing nodes without claiming domain-grounded identities. Our contribution is methodological: a reproducible insertion protocol and evaluation lens for incomplete network science and engineering

Problem

• controlled node insertion
• incomplete complex networks
• graph generation
• network topology
• node generation

Innovation

• controlled node insertion
• variational graph autoencoder
• incomplete networks
• graph generation
• topology preservation