118 citations · 440 across the 36 of their papers we have counts for
6 papers · 1 filter
Principal Graph Encoder Embedding and Principal Community Detection
Cencheng Shen, Yuexiao Dong, Carey E. Priebe +3
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves ver…
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Cencheng Shen, Jonathan Larson, Ha Trinh +3
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies,…
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…
Multiplex graph matching matched filters
Konstantinos Pantazis, Daniel L. Sussman, Youngser Park +3
We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the framework of Sussman et al…