We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs....
Graph-based modeling has emerged as a powerful abstraction capable of capturing in a single and unified framework many of the relational, spatial, topological, and other characteri...
In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landm...
Michalis Potamias, Francesco Bonchi, Carlos Castil...
We present a novel hierarchical force-directed method for drawing large graphs. Given a graph G = (V,E), the algorithm produces an embedding for G in an Euclidean space E of any d...
Pawel Gajer, Michael T. Goodrich, Stephen G. Kobou...
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures bu...