Community sub graphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs, devising a novel concept of k-cores on weighted graphs. We applied the k-core approach on large real world graphs - such as DBLP and report interesting results.
%0 Conference Paper
%1 giatsidis2011evaluating
%A Giatsidis, C.
%A Thilikos, D.M.
%A Vazirgiannis, M.
%B Proc. Int. Conf. Advances in Social Networks Analysis and Mining
%D 2011
%K community core graph
%P 87--93
%R 10.1109/ASONAM.2011.65
%T Evaluating Cooperation in Communities with the k-Core Structure
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5992567&tag=1
%X Community sub graphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs, devising a novel concept of k-cores on weighted graphs. We applied the k-core approach on large real world graphs - such as DBLP and report interesting results.
@inproceedings{giatsidis2011evaluating,
abstract = {Community sub graphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs, devising a novel concept of k-cores on weighted graphs. We applied the k-core approach on large real world graphs - such as DBLP and report interesting results.},
added-at = {2013-08-27T12:56:29.000+0200},
author = {Giatsidis, C. and Thilikos, D.M. and Vazirgiannis, M.},
biburl = {https://www.bibsonomy.org/bibtex/2e37c82bd47aadcbc0a72e57020fa6041/jaeschke},
booktitle = {Proc. Int. Conf. Advances in Social Networks Analysis and Mining},
doi = {10.1109/ASONAM.2011.65},
interhash = {ccc1059e51e6c0671f9987824e7c9f92},
intrahash = {e37c82bd47aadcbc0a72e57020fa6041},
keywords = {community core graph},
pages = {87--93},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Evaluating Cooperation in Communities with the k-Core Structure},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5992567&tag=1},
year = 2011
}