Self-Organizing Graphs - A Neural Network Perspective of Graph Layout

Meyer, Bernd (1998) Self-Organizing Graphs - A Neural Network Perspective of Graph Layout. In: Graph Drawing 6th International Symposium, GD’ 98, August 13-15, 1998 , pp. 246-262(Official URL: http://dx.doi.org/10.1007/3-540-37623-2_19).

Full text not available from this repository.

Abstract

The paper presents self-organizing graphs, a novel approach to graph layout based on a competitive learning algorithm. This method is an extension of self-organization strategies known from unsupervised neural networks, namely from Kohonen's self-organizing map. Its main advantage is that it is very flexibly adaptable to arbitrary types of visualization spaces, for it is explicitly parameterized by a metric model of the layout space. Yet the method consumes comparatively little computational resources and does not need any heavy-duty preprocessing. Unlike with other stochastic layout algorithms, not even the costly repeated evaluation of an objective function is required. To our knowledge this is the first connectionist approach to graph layout. The paper presents applications to 2D-layout as well as to 3D-layout and to layout in arbitrary metric spaces, such as networks on spherical surfaces.

Item Type: Conference Paper
Additional Information: 10.1007/3-540-37623-2_19
Classifications: M Methods > M.300 Dynamic / Incremental / Online
G Algorithms and Complexity > G.999 Others
M Methods > M.900 Tree
P Styles > P.060 3D
URI: http://gdea.informatik.uni-koeln.de/id/eprint/311

Actions (login required)

View Item View Item