118 citations · 291 across the 26 of their papers we have counts for
31 papers
Attractor-Based Coevolving Dot Product Random Graph Model
Shiwen Yang, Daniel L. Sussman
We introduce the attractor-based coevolving dot product random graph model (ABCDPRGM) to analyze time-series network data manifesting polarizing or flocking behavior. Graphs are ge…
Gotta match 'em all: Solution diversification in graph matching matched filters
Zhirui Li, Ben Johnson, Daniel L. Sussman +2
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter techniq…
Shuffled total least squares
Qian Wang, Daniel Sussman
Linear regression with shuffled labels and with a noisy latent design matrix arises in many correspondence recovery problems. We propose a total least-squares approach to the probl…
Unbiased estimation for additive exposure models
Kelly Kung, Daniel L. Sussman
Causal inference methods have been applied in various fields where researchers want to estimate treatment effects. In traditional causal inference settings, one assumes that the ou…
iGraphMatch: an R Package for the Analysis of Graph Matching
Zihuan Qiao, Daniel Sussman
iGraphMatch is an R package for finding corresponding vertices between two graphs, also known as graph matching. The package implements three categories of prevalent graph matching…
Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings
Christy Lin, Daniel Sussman, Prakash Ishwar
Random walk based node embedding algorithms learn vector representations of nodes by optimizing an objective function of node embedding vectors and skip-bigram statistics computed…