|Title||Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Stamos, Konstantinos, Nikolaos A. Laskaris, and Athena Vakali|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics, Part C|
|Keywords||Laplacian eigenmap, large scale, manifold learning, spectral graph theory, web communities|
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.
Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation