Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
Motivated by structural properties of the Web graph that support efficient data structures for in memory adjacency queries, we study the extent to which a large network can be com...
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, ...
We present a family of algorithms to uncover tribes--groups of individuals who share unusual sequences of affiliations. While much work inferring community structure describes lar...
While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters...
The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. This is especially important in the field of privacy...