activity
20092016
most citedEigenvector Synchronization, Graph Rigidity and the Molecule Problem

43 citations · 118 across the 9 of their papers we have counts for

collaborators

11 papers

cs.SI2016★ 4 cited

Scalable Constrained Clustering: A Generalized Spectral Method

Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla +2

We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacia…

cs.SI2015★ 24 cited

Synchronization over and community detection in multiplex signed networks with constraints

Mihai Cucuringu

Finding group elements from noisy measurements of their pairwise ratios is also known as the group synchronization problem, first introduced in the context of the group SO(2) of pl…

cs.CE2015

ADM-CLE approach for detecting slow variables in continuous time Markov chains and dynamic data

Mihai Cucuringu, Radek Erban

A method for detecting intrinsic slow variables in high-dimensional stochastic chemical reaction networks is developed and analyzed. It combines anisotropic diffusion maps (ADM) wi…

cs.LG2015★ 12 cited

Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and Semidefinite Programming Synchronization

Mihai Cucuringu

We consider the classic problem of establishing a statistical ranking of a set of n items given a set of inconsistent and incomplete pairwise comparisons between such items. Instan…

stat.ML2015★ 1 cited

Point Localization and Density Estimation from Ordinal kNN graphs using Synchronization

Mihai Cucuringu, Joseph Woodworth

We consider the problem of embedding unweighted, directed k-nearest neighbor graphs in low-dimensional Euclidean space. The k-nearest neighbors of each vertex provides ordinal info…

cs.SI2015★ 6 cited

Scalable Constrained Clustering: A Generalized Spectral Method

Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla

We present a principled spectral approach to the well-studied constrained clustering problem. It reduces clustering to a generalized eigenvalue problem on Laplacians. The method wo…