We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at such a large scale, we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks. Using a methodology based on the maximum-likelihood principle, we investigate a wide variety of network formation strategies, and show that edge locality plays a critical role in evolution of networks. Our findings supplement earlier network models based on the inherently non-local preferential attachment.
%0 Conference Paper
%1 leskovec08
%A Leskovec, Jure
%A Backstrom, Lars
%A Kumar, Ravi
%A Tomkins, Andrew
%B KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
%C New York, NY, USA
%D 2008
%I ACM
%K closure evolution flickr linkedin network social triadic yahoo
%P 462--470
%R 10.1145/1401890.1401948
%T Microscopic evolution of social networks
%U http://dx.doi.org/10.1145/1401890.1401948
%X We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at such a large scale, we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks. Using a methodology based on the maximum-likelihood principle, we investigate a wide variety of network formation strategies, and show that edge locality plays a critical role in evolution of networks. Our findings supplement earlier network models based on the inherently non-local preferential attachment.
%@ 978-1-60558-193-4
@inproceedings{leskovec08,
abstract = {We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at such a large scale, we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks. Using a methodology based on the maximum-likelihood principle, we investigate a wide variety of network formation strategies, and show that edge locality plays a critical role in evolution of networks. Our findings supplement earlier network models based on the inherently non-local preferential attachment.},
added-at = {2012-02-27T15:52:02.000+0100},
address = {New York, NY, USA},
author = {Leskovec, Jure and Backstrom, Lars and Kumar, Ravi and Tomkins, Andrew},
biburl = {https://www.bibsonomy.org/bibtex/2e1743c9b9fde98839206a475bbed1bf0/psinger},
booktitle = {KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining},
citeulike-article-id = {3352998},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1401890.1401948},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/1401890.1401948},
doi = {10.1145/1401890.1401948},
interhash = {e6896bf5621609ddb653fffe38585cad},
intrahash = {e1743c9b9fde98839206a475bbed1bf0},
isbn = {978-1-60558-193-4},
keywords = {closure evolution flickr linkedin network social triadic yahoo},
location = {Las Vegas, Nevada, USA},
pages = {462--470},
posted-at = {2009-07-08 12:22:21},
priority = {2},
publisher = {ACM},
timestamp = {2012-03-19T13:37:12.000+0100},
title = {Microscopic evolution of social networks},
url = {http://dx.doi.org/10.1145/1401890.1401948},
year = 2008
}