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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2605.09446 |
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| _version_ | 1866915998872895488 |
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| author | Jalali, Mehrdad Vu, Binh Chandna, Swati Ding, Chen |
| author_facet | Jalali, Mehrdad Vu, Binh Chandna, Swati Ding, Chen |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09446 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Astro Generative Network: A Variational Framework for Controlled Node Insertion in Incomplete Complex Networks Jalali, Mehrdad Vu, Binh Chandna, Swati Ding, Chen Social and Information Networks 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 |
| title | Astro Generative Network: A Variational Framework for Controlled Node Insertion in Incomplete Complex Networks |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2605.09446 |