<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Laskaris, Nikolaos A.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Systems, Man, and Cybernetics, Part C</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Laplacian eigenmap</style></keyword><keyword><style  face="normal" font="default" size="100%">large scale</style></keyword><keyword><style  face="normal" font="default" size="100%">manifold learning</style></keyword><keyword><style  face="normal" font="default" size="100%">spectral graph theory</style></keyword><keyword><style  face="normal" font="default" size="100%">web communities</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">879-888</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
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