23 citations · 100 across the 27 of their papers we have counts for
5 papers · 2 filters
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Keith Levin, Fred Roosta, Minh Tang +2
Graph embeddings, a class of dimensionality reduction techniques designed for relational data, have proven useful in exploring and modeling network structure. Most dimensionality r…
Geodesic Learning via Unsupervised Decision Forests
Meghana Madhyastha, Percy Li, James Browne +4
Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic d…
Vertex Classification on Weighted Networks
Hayden Helm, Joshua Vogelstein, Carey Priebe
This paper proposes a discrimination technique for vertices in a weighted network. We assume that the edge weights and adjacencies in the network are conditionally independent and…
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Joshua Agterberg, Youngser Park, Jonathan Larson +3
Given a pair of graphs and and a vertex set of interest in , the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in (if t…
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
Congyuan Yang, Carey E. Priebe, Youngser Park +1
Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of t…