dorsal/arxiv
View SchemaA Gap in the Community-Size Distribution of a Large-Scale Social Networking Site
| Authors | Kikuo Yuta, Naoaki Ono, Yoshi Fujiwara |
|---|---|
| Categories | |
| ArXiv ID | physics/0701168 |
| URL | https://arxiv.org/abs/physics/0701168 |
Abstract
Social networking sites (SNS) have recently used by millions of people all over the world. An SNS is a society on the Internet, where people communicate and foster friendship with each other. We examine a nation-wide SNS (more than six million users at present), mutually acknowledged friendship network with third million people and nearly two million links. By employing a community-extracting method developed by Newman and others, we found that there exists a range of community-sizes in which only few communities are detected. This novel feature cannot be explained by previous growth models of networks. We present a simple model with two processes of acquaintance, connecting nearest neighbors and random linkage. We show that the model can explain the gap in the community-size distribution as well as other statistical properties including long-tail degree distribution, high transitivity, its correlation with degree, and degree-degree correlation. The model can estimate how the two processes, which are ubiquitous in many social networks, are working with relative frequencies in the SNS as well as other societies.
{
"annotation_id": "d47b3ef6-fe44-4b25-a331-6e7abc869c72",
"date_created": "2026-03-02T18:01:17.628000Z",
"date_modified": "2026-03-02T18:01:17.628000Z",
"file_hash": "912552d631bf3e48cbfa5a098cf64920c363e6db30eb22a68db28800c98ccfb1",
"private": false,
"record": {
"abstract": "Social networking sites (SNS) have recently used by millions of people all\nover the world. An SNS is a society on the Internet, where people communicate\nand foster friendship with each other. We examine a nation-wide SNS (more than\nsix million users at present), mutually acknowledged friendship network with\nthird million people and nearly two million links. By employing a\ncommunity-extracting method developed by Newman and others, we found that there\nexists a range of community-sizes in which only few communities are detected.\nThis novel feature cannot be explained by previous growth models of networks.\nWe present a simple model with two processes of acquaintance, connecting\nnearest neighbors and random linkage. We show that the model can explain the\ngap in the community-size distribution as well as other statistical properties\nincluding long-tail degree distribution, high transitivity, its correlation\nwith degree, and degree-degree correlation. The model can estimate how the two\nprocesses, which are ubiquitous in many social networks, are working with\nrelative frequencies in the SNS as well as other societies.",
"arxiv_id": "physics/0701168",
"authors": [
"Kikuo Yuta",
"Naoaki Ono",
"Yoshi Fujiwara"
],
"categories": [
"physics.soc-ph",
"cs.CY",
"physics.data-an"
],
"title": "A Gap in the Community-Size Distribution of a Large-Scale Social Networking Site",
"url": "https://arxiv.org/abs/physics/0701168"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "0024e404-eb0c-49f0-8138-5629d8e20835",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}