118 citations · 1.3k across the 129 of their papers we have counts for
9 papers · 1 filter
Synergistic Graph Fusion via Encoder Embedding
Cencheng Shen, Carey E. Priebe, Jonathan Larson +1
In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our meth…
Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
Cencheng Shen, Youngser Park, Carey E. Priebe
In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a norma…
Dynamic Network Sampling for Community Detection
Cong Mu, Youngser Park, Carey E. Priebe
We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph.…
Random line graphs and edge-attributed network inference
Zachary Lubberts, Avanti Athreya, Youngser Park +1
We extend the latent position random graph model to the line graph of a random graph, which is formed by creating a vertex for each edge in the original random graph, and connectin…
Vertex nomination between graphs via spectral embedding and quadratic programming
Runbing Zheng, Vince Lyzinski, Carey E. Priebe +1
Given a network and a subset of interesting vertices whose identities are only partially known, the vertex nomination problem seeks to rank the remaining vertices in such a way tha…
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
Cong Mu, Angelo Mele, Lingxin Hao +3
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.…