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

TL;DR

The paper introduces Astro Generative Network, a variational framework for controlled node insertion in incomplete networks, enabling plausible graph extension while preserving global topology.

Contribution

It presents a reproducible protocol and evaluation method for inserting nodes into incomplete networks, advancing graph generation and analysis techniques.

Findings

AGN maintains modest changes in clustering and modularity after node insertion.

Disabling generated-generated edges removes artificial density artifacts.

AGN effectively separates new nodes from existing ones without claiming specific identities.

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