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.
%0 Generic
%1 citeulike:1176989
%A Yuta, Kikuo
%A Ono, Naoaki
%A Fujiwara, Yoshi
%D 2007
%K large-scale networking site social
%T A Gap in the Community-Size Distribution of a Large-Scale Social Networking Site
%U http://arxiv.org/abs/physics/0701168
%X 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.
@misc{citeulike:1176989,
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.},
added-at = {2007-08-18T13:22:24.000+0200},
author = {Yuta, Kikuo and Ono, Naoaki and Fujiwara, Yoshi},
biburl = {https://www.bibsonomy.org/bibtex/2083b8b458d3378ceefe804dde5528036/a_olympia},
citeulike-article-id = {1176989},
description = {citeulike},
eprint = {physics/0701168},
interhash = {47eb20767e250fddbc717cb70c894ccc},
intrahash = {083b8b458d3378ceefe804dde5528036},
keywords = {large-scale networking site social},
month = Mar,
priority = {2},
timestamp = {2007-08-18T13:22:30.000+0200},
title = {A Gap in the Community-Size Distribution of a Large-Scale Social Networking Site},
url = {http://arxiv.org/abs/physics/0701168},
year = 2007
}