%0 Conference Paper %1 yuruk08networks %A Yuruk, Nurcan %A Xu, Xiaowei %A Schweiger, Thomas A.J. %B Hawaii International Conference on System Sciences, Proceedings of the 41st Annual %D 2008 %K research.clustering research.conceptual.graphs research.web20.communities %P 143-143 %R 10.1109/HICSS.2008.331 %T On Structural Analysis of Large Networks %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4438696&arnumber=4438846&count=502&index=149 %X Containing much valuable information, networks such as the World Wide Web, social networks and metabolic networks draw increasingly attention in scientific communities. Network clustering (or graph partitioning) is the discovery of underlying clusters of related vertices in networks. But beyond organizing vertices into clusters of peers is the question of what role each vertex play in the network. This paper presents some new ways of uncovering underlying structures, including the roles that vertices play in the network. Identifying vertex roles is useful for applications such as viral marketing and epidemiology. For example, hubs are responsible for spreading ideas or disease. We applied our algorithm to analyze some real networks. The results demonstrate a superior performance over other methods such as modularity-based algorithms. %@ 978-0-7695-3075-8

@inproceedings{yuruk08networks, abstract = {Containing much valuable information, networks such as the World Wide Web, social networks and metabolic networks draw increasingly attention in scientific communities. Network clustering (or graph partitioning) is the discovery of underlying clusters of related vertices in networks. But beyond organizing vertices into clusters of peers is the question of what role each vertex play in the network. This paper presents some new ways of uncovering underlying structures, including the roles that vertices play in the network. Identifying vertex roles is useful for applications such as viral marketing and epidemiology. For example, hubs are responsible for spreading ideas or disease. We applied our algorithm to analyze some real networks. The results demonstrate a superior performance over other methods such as modularity-based algorithms.}, added-at = {2008-04-26T13:03:19.000+0200}, author = {Yuruk, Nurcan and Xu, Xiaowei and Schweiger, Thomas A.J.}, biburl = {https://www.bibsonomy.org/bibtex/298a676e20efe8b9447a8f703e4bae4f1/msn}, booktitle = {Hawaii International Conference on System Sciences, Proceedings of the 41st Annual}, description = {Welcome to IEEE Xplore 2.0: On Structural Analysis of Large Networks}, doi = {10.1109/HICSS.2008.331}, interhash = {58d9a87999da40cab88d9b188e4da2d1}, intrahash = {98a676e20efe8b9447a8f703e4bae4f1}, isbn = {978-0-7695-3075-8}, issn = {1530-1605}, keywords = {research.clustering research.conceptual.graphs research.web20.communities}, pages = {143-143}, timestamp = {2009-06-25T15:59:15.000+0200}, title = {On Structural Analysis of Large Networks}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4438696&arnumber=4438846&count=502&index=149}, year = 2008 }