Getting an overview of a large online social net-work and deciding which communities to join is a challenging task for a new user. We propose a method that maps a large network into a smaller graph with two kinds of nodes: a node of the first kind is representative of a community, a node of the second kind is neighbor to a representative and rejects the semantics of that community. Our approach encompasses a learning and ranking algorithm that derives this smaller graph from the original one, and a visualization algorithm that returns a graph layout to the observer. We report on our results on inspecting the network of a folksonomy.
%0 Conference Paper
%1 6040714
%A Gabriel, H.
%A Spiliopoulou, M.
%A Stachtiari, E.
%A Vakali, A.
%B Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on
%D 2011
%K kmd
%P 475-478
%R 10.1109/WI-IAT.2011.77
%T Summarization Meets Visualization on Online Social Networks
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6040714
%V 1
%X Getting an overview of a large online social net-work and deciding which communities to join is a challenging task for a new user. We propose a method that maps a large network into a smaller graph with two kinds of nodes: a node of the first kind is representative of a community, a node of the second kind is neighbor to a representative and rejects the semantics of that community. Our approach encompasses a learning and ranking algorithm that derives this smaller graph from the original one, and a visualization algorithm that returns a graph layout to the observer. We report on our results on inspecting the network of a folksonomy.
@inproceedings{6040714,
abstract = {Getting an overview of a large online social net-work and deciding which communities to join is a challenging task for a new user. We propose a method that maps a large network into a smaller graph with two kinds of nodes: a node of the first kind is representative of a community, a node of the second kind is neighbor to a representative and rejects the semantics of that community. Our approach encompasses a learning and ranking algorithm that derives this smaller graph from the original one, and a visualization algorithm that returns a graph layout to the observer. We report on our results on inspecting the network of a folksonomy.},
added-at = {2014-06-20T12:23:05.000+0200},
author = {Gabriel, H. and Spiliopoulou, M. and Stachtiari, E. and Vakali, A.},
biburl = {https://www.bibsonomy.org/bibtex/2e6971e3791fed3fefcebe3fd62a8b24e/kmd-ovgu},
booktitle = {Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on},
doi = {10.1109/WI-IAT.2011.77},
interhash = {b08185f2c4b7a5b1ed13ab6b4807e04c},
intrahash = {e6971e3791fed3fefcebe3fd62a8b24e},
keywords = {kmd},
month = aug,
pages = {475-478},
timestamp = {2014-06-20T12:23:05.000+0200},
title = {Summarization Meets Visualization on Online Social Networks},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6040714},
volume = 1,
year = 2011
}