activity
20162023
most citedEstimating Propensity Parameters using Google PageRank and Genetic Algorithms

11 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2023

Balancing between the Local and Global Structures (LGS) in Graph Embedding

Jacob Miller, Vahan Huroyan, Stephen Kobourov

We present a method for balancing between the Local and Global Structures (LGS) in graph embedding, via a tunable parameter. Some embedding methods aim to capture global structures…

cs.HC2023

On the Perception of Small Sub-graphs

Jacob Miller, Mohammad Ghoniem, Hsiang-Yun Wu +1

Interpreting a node-link graph is enhanced if similar subgraphs (or motifs) are depicted in a similar manner; that is, they have the same visual form. Small motifs within graphs ma…

cs.CG2022

Spherical Graph Drawing by Multi-dimensional Scaling

Jacob Miller, Vahan Huroyan, Stephen Kobourov

We describe an efficient and scalable spherical graph embedding method. The method uses a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to s…

cs.GR2022

Browser-based Hyperbolic Visualization of Graphs

Jacob Miller, Stephen Kobourov, Vahan Huroyan

Hyperbolic geometry offers a natural focus + context for data visualization and has been shown to underlie real-world complex networks. However, current hyperbolic network visualiz…

q-bio.MN2016★ 11 cited

Estimating Propensity Parameters using Google PageRank and Genetic Algorithms

David Murrugarra, Jacob Miller, Alex Mueller

Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity s…