%0 Journal Article
%J IEEE Transactions on Systems, Man, and Cybernetics, Part C
%D 2012
%T Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation
%A Stamos, Konstantinos
%A Laskaris, Nikolaos A.
%A Athena Vakali
%K Laplacian eigenmap
%K large scale
%K manifold learning
%K spectral graph theory
%K web communities
%X <p>The Web as a graph can be embedded in a lowdimensionalspace where its geometry can be visualized and studiedin order to mine interesting patterns such as web communities.The existing algorithms operate on small-to-medium-scalegraphs; thus, we propose a close to linear time algorithm calledMani-Web suitable for large-scale graphs. The result is similarto the one produced by the manifold-learning technique Laplacianeigenmap that is tested on artificial manifolds and real webgraphs.Mani-Web can also be used as a general-purpose manifoldlearning/dimensionality-reductiontechnique as long as the datacan be represented as a graph.</p>
%B IEEE Transactions on Systems, Man, and Cybernetics, Part C
%V 42
%P 879-888
%G eng