FADE: Graph Drawing, Clustering, and Visual Abstraction Export

@incollection{citeulike:3772317, abstract = {A fast algorithm(fade) for the 2D drawing, geometric clustering and multilevel viewing of large undirected graphs is presented. The algorithm is an extension of the Barnes-Hut hierarchical space decomposition method, which includes edges and multilevel visual abstraction. Compared to the original force directed algorithm, the time overhead is O(e + n log n) where n and e are the numbers of nodes and edges. The improvement is possible since the decomposition tree provides a systematic way to determine the degree of closeness between nodes without explicitly calculating the distance between each node. Different types of regular decomposition trees are introduced. The decomposition tree also represents a hierarchical clustering of the nodes, which improves in a graph theoretic sense as the graph drawing approaches a lower energy state. Finally, the decomposition tree provides a mechanism to view the hierarchical clustering on various levels of abstraction. Larger graphs can be represented more concisely, on a higher level of abstraction, with fewer graphics on screen.}, author = {Quigley, Aaron and Eades, Peter}, citeulike-article-id = {3772317}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-44541-2_19}, citeulike-linkout-1 = {http://www.springerlink.com/content/mlbkplfwx7cmk4hf}, doi = {10.1007/3-540-44541-2_19}, journal = {Graph Drawing}, keywords = {clustering, graph\_layout, network, visualization}, pages = {77--80}, posted-at = {2008-12-11 06:04:06}, priority = {2}, title = {FADE: Graph Drawing, Clustering, and Visual Abstraction}, url = {http://dx.doi.org/10.1007/3-540-44541-2_19}, year = {2001} }

TY - CHAP ID - citeulike:3772317 L3 - citeulike-article-id:3772317 N2 - A fast algorithm(fade) for the 2D drawing, geometric clustering and multilevel viewing of large undirected graphs is presented. The algorithm is an extension of the Barnes-Hut hierarchical space decomposition method, which includes edges and multilevel visual abstraction. Compared to the original force directed algorithm, the time overhead is O(e + n log n) where n and e are the numbers of nodes and edges. The improvement is possible since the decomposition tree provides a systematic way to determine the degree of closeness between nodes without explicitly calculating the distance between each node. Different types of regular decomposition trees are introduced. The decomposition tree also represents a hierarchical clustering of the nodes, which improves in a graph theoretic sense as the graph drawing approaches a lower energy state. Finally, the decomposition tree provides a mechanism to view the hierarchical clustering on various levels of abstraction. Larger graphs can be represented more concisely, on a higher level of abstraction, with fewer graphics on screen. JF - Graph Drawing EP - 80 TI - FADE: Graph Drawing, Clustering, and Visual Abstraction SP - 77 KW - clustering KW - graph_layout KW - network KW - visualization AU - Quigley, Aaron AU - Eades, Peter PY - 2001/// UR - http://dx.doi.org/10.1007/3-540-44541-2_19 ER -