169 citations · 198 across the 10 of their papers we have counts for
11 papers
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,…
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…
Maximum likelihood estimation for mechanistic network models
Jonathan Larson, Jukka-Pekka Onnela
Mechanistic network models specify the mechanisms by which networks grow and change, allowing researchers to investigate complex systems using both simulation and analytical techni…
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Tiona Zuzul, Emily Cox Pahnke, Jonathan Larson +9
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work. To understand these shifts, we analyzed…
Learning without gradient descent encoded by the dynamics of a neurobiological model
Vivek Kurien George, Vikash Morar, Weiwei Yang +6
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss functi…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…