Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation

The Web as a graph can be embedded in a low-dimensional space where its geometry can be visualized and studied in order to mine interesting patterns such as web communities. The existing algorithms operate on small-to-medium-scale graphs; thus, we propose a close to linear time algorithm called Mani-Web suitable for large-scale graphs. The result is similar to the one produced by the manifold-learning technique Laplacian eigenmap that is tested on artificial manifolds and real web-graphs. Mani-Web can also be used as a general-purpose manifold-learning/dimensionality-reduction technique as long as the data can be represented as a graph.