MaxEnt frameworks for weighted, temporal, multiplex, and higher‑order networks
Develop maximum-entropy ensembles for weighted and signed networks, temporal networks, multiplex (multilayer) networks, and higher-order interaction structures (hypergraphs and simplicial complexes) that remain analytically tractable, scalable, and compatible with geometric embeddings and renormalization, extending the existing fermionic MaxEnt framework for binary pairwise graphs and, where appropriate, formulating bosonic analogues for multi-edge or weighted descriptions.
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Increasing realism in the description of real networks brings additional layers of information---weights and signs on links, temporal evolution, multiplex structure, and higher-order interactions---which demand new generative models and inference tools that retain interpretability while balancing accuracy against computational and conceptual complexity. Extending maximum-entropy approaches to these settings is a largely open frontier: while binary, pairwise ensembles admit a clean fermionic interpretation, weighted or multi-edge descriptions naturally suggest bosonic analogues, and higher-order interactions call for ensembles defined on hyperedges or simplices rather than on links.