11 citations · 17 across the 5 of their papers we have counts for
8 papers
The decomposition of the higher-order homology embedding constructed from the -Laplacian
Yu-Chia Chen, Marina Meilă
The null space of the -th order Laplacian , known as the {\em -th homology vector space}, encodes the non-trivial topology of a manifold or a network.…
A class of network models recoverable by spectral clustering
Yali Wan, Marina Meila
Finding communities in networks is a problem that remains difficult, in spite of the amount of attention it has recently received. The Stochastic Block-Model (SBM) is a generative…
Guarantees for Hierarchical Clustering by the Sublevel Set method
Marina Meila
Meila (2018) introduces an optimization based method called the Sublevel Set method, to guarantee that a clustering is nearly optimal and "approximately correct" without relying on…
Measuring the Robustness of Graph Properties
Yali Wan, Marina Meila
In this paper, we propose a perturbation framework to measure the robustness of graph properties. Although there are already perturbation methods proposed to tackle this problem, t…
How to sample connected -partitions of a graph
Marina Meila
A connected undirected graph is given. This paper presents an algorithm that samples (non-uniformly) a partition of the graph nodes , such that th…
megaman: Manifold Learning with Millions of points
James McQueen, Marina Meila, Jacob VanderPlas +1
Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are, at least in theory,…