dorsal/arxiv
View SchemaAnalytical Solution of a Stochastic Content Based Network Model
| Authors | Muhittin Mungan, Alkan Kabakcioglu, Duygu Balcan, Ayse Erzan |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0406049 |
| URL | https://arxiv.org/abs/q-bio/0406049 |
| DOI | 10.1088/0305-4470/38/44/001 |
| Journal | J. Phys. A: Math. Gen. 38 (2005) 9599--9620 |
Abstract
We define and completely solve a content-based directed network whose nodes consist of random words and an adjacency rule involving perfect or approximate matches, for an alphabet with an arbitrary number of letters. The analytic expression for the out-degree distribution shows a crossover from a leading power law behavior to a log-periodic regime bounded by a different power law decay. The leading exponents in the two regions have a weak dependence on the mean word length, and an even weaker dependence on the alphabet size. The in-degree distribution, on the other hand, is much narrower and does not show scaling behavior. The results might be of interest for understanding the emergence of genomic interaction networks, which rely, to a large extent, on mechanisms based on sequence matching, and exhibit similar global features to those found here.
{
"annotation_id": "6f555be8-8ca1-4966-b9e6-83398f5a864c",
"date_created": "2026-03-02T18:01:32.295000Z",
"date_modified": "2026-03-02T18:01:32.295000Z",
"file_hash": "ce03de67199cff19051cf189a685ca2c651f8adb22e1a3ac7a4b8357efbaf2ed",
"private": false,
"record": {
"abstract": "We define and completely solve a content-based directed network whose nodes\nconsist of random words and an adjacency rule involving perfect or approximate\nmatches, for an alphabet with an arbitrary number of letters. The analytic\nexpression for the out-degree distribution shows a crossover from a leading\npower law behavior to a log-periodic regime bounded by a different power law\ndecay. The leading exponents in the two regions have a weak dependence on the\nmean word length, and an even weaker dependence on the alphabet size. The\nin-degree distribution, on the other hand, is much narrower and does not show\nscaling behavior. The results might be of interest for understanding the\nemergence of genomic interaction networks, which rely, to a large extent, on\nmechanisms based on sequence matching, and exhibit similar global features to\nthose found here.",
"arxiv_id": "q-bio/0406049",
"authors": [
"Muhittin Mungan",
"Alkan Kabakcioglu",
"Duygu Balcan",
"Ayse Erzan"
],
"categories": [
"q-bio.MN",
"cond-mat.stat-mech",
"q-bio.GN"
],
"doi": "10.1088/0305-4470/38/44/001",
"journal_ref": "J. Phys. A: Math. Gen. 38 (2005) 9599--9620",
"title": "Analytical Solution of a Stochastic Content Based Network Model",
"url": "https://arxiv.org/abs/q-bio/0406049"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "8a755717-1053-46ab-b41b-554a007ff2fd",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}