A General Algorithm for Detecting Higher-Order Interactions via Random Sequential Additions
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911316122271744 |
|---|---|
| author | Shamail, Ahmad McWhite, Claire |
| author_facet | Shamail, Ahmad McWhite, Claire |
| contents | Many systems exhibit complex interactions between their components: some features or actions amplify each other's effects, others provide redundant information, and some contribute independently. We present a simple geometric method for discovering interactions and redundancies: when elements are added in random sequential orders and their contributions plotted over many trials, characteristic L-shaped patterns emerge that directly reflect interaction structure. The approach quantifies how the contribution of each element depends on those added before it, revealing patterns that distinguish interaction, independence, and redundancy on a unified scale. When pairwise contributions are visualized as two--dimensional point clouds, redundant pairs form L--shaped patterns where only the first-added element contributes, while synergistic pairs form L--shaped patterns where only elements contribute together. Independent elements show order--invariant distributions. We formalize this with the L--score, a continuous measure ranging from $-1$ (perfect synergy, e.g. $Y=X_1X_2$) to $0$ (independence) to $+1$ (perfect redundancy, $X_1 \approx X_2$). The relative scaling of the L--shaped arms reveals feature dominance in which element consistently provides more information. Although computed only from pairwise measurements, higher--order interactions among three or more elements emerge naturally through consistent cross--pair relationships (e.g. AB, AC, BC). The method is metric--agnostic and broadly applicable to any domain where performance can be evaluated incrementally over non-repeating element sequences, providing a unified geometric approach to uncovering interaction structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11793 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A General Algorithm for Detecting Higher-Order Interactions via Random Sequential Additions Shamail, Ahmad McWhite, Claire Machine Learning Many systems exhibit complex interactions between their components: some features or actions amplify each other's effects, others provide redundant information, and some contribute independently. We present a simple geometric method for discovering interactions and redundancies: when elements are added in random sequential orders and their contributions plotted over many trials, characteristic L-shaped patterns emerge that directly reflect interaction structure. The approach quantifies how the contribution of each element depends on those added before it, revealing patterns that distinguish interaction, independence, and redundancy on a unified scale. When pairwise contributions are visualized as two--dimensional point clouds, redundant pairs form L--shaped patterns where only the first-added element contributes, while synergistic pairs form L--shaped patterns where only elements contribute together. Independent elements show order--invariant distributions. We formalize this with the L--score, a continuous measure ranging from $-1$ (perfect synergy, e.g. $Y=X_1X_2$) to $0$ (independence) to $+1$ (perfect redundancy, $X_1 \approx X_2$). The relative scaling of the L--shaped arms reveals feature dominance in which element consistently provides more information. Although computed only from pairwise measurements, higher--order interactions among three or more elements emerge naturally through consistent cross--pair relationships (e.g. AB, AC, BC). The method is metric--agnostic and broadly applicable to any domain where performance can be evaluated incrementally over non-repeating element sequences, providing a unified geometric approach to uncovering interaction structure. |
| title | A General Algorithm for Detecting Higher-Order Interactions via Random Sequential Additions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.11793 |