Multiscale visualization of small world networks

@inproceedings{citeulike:221103, abstract = {Many networks under study in information visualization are "small world" networks. These networks first appeared in the study of social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a network of groups that are themselves small world networks. We describe a metric that has been designed in order to identify the weakest edges in a small world network leading to an easy and low cost filtering procedure that breaks up a graph into smaller and highly connected components. We show how this metric can be exploited through an interactive navigation of the network based on semantic zooming. Once the network is decomposed into a hierarchy of sub-networks, a user can easily find groups and subgroups of actors and understand their dynamics.}, author = {Auber, D. and Chiricota, Y. and Jourdan, F. and Melancon, G. }, citeulike-article-id = {221103}, journal = {Information Visualization, 2003. INFOVIS 2003. IEEE Symposium on}, keywords = {multiscale, networks, small, visualization, world}, pages = {75--81}, posted-at = {2006-12-18 20:23:44}, priority = {0}, title = {Multiscale visualization of small world networks}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1249011}, year = {2003} }

TY - CONF ID - citeulike:221103 L3 - citeulike-article-id:221103 TI - Multiscale visualization of small world networks JF - Information Visualization, 2003. INFOVIS 2003. IEEE Symposium on SP - 75 EP - 81 N2 - Many networks under study in information visualization are "small world" networks. These networks first appeared in the study of social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a network of groups that are themselves small world networks. We describe a metric that has been designed in order to identify the weakest edges in a small world network leading to an easy and low cost filtering procedure that breaks up a graph into smaller and highly connected components. We show how this metric can be exploited through an interactive navigation of the network based on semantic zooming. Once the network is decomposed into a hierarchy of sub-networks, a user can easily find groups and subgroups of actors and understand their dynamics. KW - multiscale KW - networks KW - small KW - visualization KW - world AU - Auber, D AU - Chiricota, Y AU - Jourdan, F AU - Melancon, G PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1249011 ER -