Vector Space of Cycles
Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represent feedback primarily through node-level dependencies, making large-scale recurrent organization difficult to estimate and compare. This limitation is particularly acute in biological and neural systems, where interactions are highly recurrent and involve many overlapping cycles. We introduce a variational framework for statistical inference on cyclic interactions. Directed interactions are represented as edge flows on a simplicial complex and evolved under an energy-minimizing dynamical system. The resulting dynamics separate transient inte...
| Authors | Moo K. Chung, Anass B. El-Yaagoubi, Hernando Ombao |
|---|---|
| Primary category | stat.ML |
| Published | 6 June 2026 |
| Last updated at source | 7 August 2026 |
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