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KDD
2008
ACM
147views Data Mining» more  KDD 2008»
14 years 11 months ago
Mobile call graphs: beyond power-law and lognormal distributions
We analyze a massive social network, gathered from the records of a large mobile phone operator, with more than a million users and tens of millions of calls. We examine the distr...
Mukund Seshadri, Sridhar Machiraju, Ashwin Sridhar...
KDD
2008
ACM
284views Data Mining» more  KDD 2008»
14 years 11 months ago
Community evolution in dynamic multi-mode networks
A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode netw...
Lei Tang, Huan Liu, Jianping Zhang, Zohreh Nazeri
KDD
2009
ACM
173views Data Mining» more  KDD 2009»
14 years 11 months ago
Constant-factor approximation algorithms for identifying dynamic communities
We propose two approximation algorithms for identifying communities in dynamic social networks. Communities are intuitively characterized as "unusually densely knit" sub...
Chayant Tantipathananandh, Tanya Y. Berger-Wolf
APVIS
2006
14 years 5 days ago
How people read sociograms: a questionnaire study
Visualizing social network data into sociograms plays an important role in communicating information about network characteristics. Previous studies have shown that human percepti...
Weidong Huang, Seok-Hee Hong, Peter Eades
ECSCW
2007
14 years 6 days ago
Making the Home Network at Home: Digital Housekeeping
This paper exploits ethnographic findings to build on and elaborate Grinter et al’s 2005 study of “the work to make the home network work”. We focus particularly on the work ...
Peter Tolmie, Andy Crabtree, Tom Rodden, Chris Gre...