Epiplexity And Graph Wiring An Empirical Study for the design of a generic algorithm

Epiplexity And Graph Wiring An Empirical Study for the design of a generic algorithm | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 March 2026 V1 Latest version Share on Epiplexity And Graph Wiring An Empirical Study for the design of a generic algorithm Author : Lorenzo Moriondo 0000-0002-8804-2963 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177430060.02394540/v1 145 views 97 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Having introduced Graph Wiring-(1) a technique that leverages the Graph Laplacian computed in feature space to provide semantically-aware search over high-dimensional vector corpora-and MRR-Top0 (2) as a topology-aware retrieval metric for evaluating it; this paper proceeds to demonstrate formally and empirically that the feature-space Laplacian produced by ArrowSpace (3) carries structural information in the sense of epiplexity (4), so that it is not plain graph metadata but a reusable context-bound semantic artifact. Using the CVE 1999-2025 vulnerability corpus as a case study, we instantiate ArrowSpace as a spectral vector-search engine, wrap its feature-space Laplacian in a Laplacian-constrained Gaussian Markov Random Field (LGMRF), and evaluate the resulting two-part Minimum Description Length (MDL) code. The model achieves a compression ratio of 38.4× over raw float32 storage, passes all three structural-information diagnostic tests. Furthermore it is demonstrated that the same Laplacian object as computed by Graph Wiring supports six distinct algorithm families (search, classification, anomaly detection, diffusion, dimensionality reduction, and data valuation) without additional learning. The two accompanying Jupyter notebooks are intended as a reproducible reference pattern for applying epiplexity measurement in algorithm design for large-scale data engineering for LLMs and ML operations. Supplementary Material File (epiplexity_a_measure_on_graph_wiring.pdf) Download 586.64 KB Information & Authors Information Version history V1 Version 1 23 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords arrowspace epiplexity graph laplacian graph wiring lgmrf mdl spectral indexing structural information vector search Authors Affiliations Lorenzo Moriondo 0000-0002-8804-2963 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 145 views 97 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lorenzo Moriondo. Epiplexity And Graph Wiring An Empirical Study for the design of a generic algorithm. Authorea . 23 March 2026. DOI: https://doi.org/10.22541/au.177430060.02394540/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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