Signed Diverse Multiplex Networks: Clustering and Inference
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Abstract (arXiv)
The paper introduces a Signed Generalized Random Dot Product Graph (SGRDPG) model, which is a variant of the Generalized Random Dot Product Graph (GRDPG), where, in addition, edges can be positive or negative. The setting is extended to a multiplex version, where all layers have the same collection of nodes and follow the SGRDPG. The only common feature of the layers of the network is that they can be partitioned into groups with common subspace structures, while otherwise matrices of connection probabilities can be all different. The setting above is extremely flexible and includes a variety of existing multiplex network models, including GRDPG, as its particular cases. By employing novel methodologies, our paper ensures strongly consistent clustering of layers and highly accurate subspace estimation, which are significant improvements over the results of Pensky and Wang (2024). All algorithms and theoretical results in the paper remain true for both signed and binary networks. In addition, the paper shows that keeping signs of the edges in the process of network construction leads to a better precision of estimation and clustering and, hence, is beneficial for tackling real world problems such as, for example, analysis of brain networks.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 30 of its 69 references that are in the map. First posted on arXiv 14 Feb 2024.
Builds on
30 of 69 references.
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18 more
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Citing papers per year: 2025: 1.
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- 2025Perfect Clustering in Very Sparse Diverse Multiplex Networks25 Jul 2025