Discovering important nodes through graph entropy: the case of Enron email database. Export

@inproceedings{shetty05discovering, abstract = {A major problem in social network analysis and link discovery is the discovery of hidden organizational structure and selection of interesting influential members based on low-level, incomplete and noisy evidence data. To address such a challenge, we exploit an information theoretic model that combines information theory with statistical techniques from area of text mining and natural language processing. The Entropy model identifies the most interesting and important nodes in a graph. We show how entropy models on graphs are relevant to study of information flow in an organiza- tion. We review the results of two different experiments which are based on entropy models. The first version of this model has been successfully tested and evaluated on the Enron email dataset}, address = {Chicago, IL, USA}, author = {Shetty, Jitesh and Adibi, Jafar}, booktitle = {Proc. of LinkKDD 2005}, citeulike-article-id = {558264}, citeulike-linkout-0 = {http://www.isi.edu/LinkKDD-05/Download/Papers/linkkdd05-11.pdf}, comment = {A measure of graph entropy, measuring the probability of links of length 2, is used to infer the importance of nodes in the network.}, day = {21}, keywords = {email, similarity, social}, month = {August}, posted-at = {2006-03-21 11:48:55}, priority = {0}, title = {Discovering important nodes through graph entropy: the case of Enron email database.}, url = {http://www.isi.edu/LinkKDD-05/Download/Papers/linkkdd05-11.pdf}, year = {2005} }

TY - CONF ID - shetty05discovering L3 - citeulike-article-id:558264 N2 - A major problem in social network analysis and link discovery is the discovery of hidden organizational structure and selection of interesting influential members based on low-level, incomplete and noisy evidence data. To address such a challenge, we exploit an information theoretic model that combines information theory with statistical techniques from area of text mining and natural language processing. The Entropy model identifies the most interesting and important nodes in a graph. We show how entropy models on graphs are relevant to study of information flow in an organiza- tion. We review the results of two different experiments which are based on entropy models. The first version of this model has been successfully tested and evaluated on the Enron email dataset AD - Chicago, IL, USA TI - Discovering important nodes through graph entropy: the case of Enron email database. T2 - Proc. of LinkKDD 2005 KW - email KW - similarity KW - social N1 - A measure of graph entropy, measuring the probability of links of length 2, is used to infer the importance of nodes in the network. AU - Shetty, Jitesh AU - Adibi, Jafar PY - 2005/08/21/ UR - http://www.isi.edu/LinkKDD-05/Download/Papers/linkkdd05-11.pdf ER -