Daniel Sussman
  • Bio
  • Advising
  • Teaching
  • Papers
  • Experience
  • Contact
  • Courses
    • MA 415/615 Spring 2025
    • MA 415/615 Spring 2024
    • MA 213 Spring 2024
    • MA 415/615 Spring 2024
    • MA 415/615 Fall 2023
    • MA 415/615 Fall 2022
    • MA 415/615 Spring 2022
    • MA 415/615 Fall 2021
    • MA 681 Fall 2021
  • Experience
  • PhD Advising
  • Publications
    • Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings
    • Estimation of the Branching Factor in Noisy Networks
    • Gotta match 'em all: Solution diversification in graph matching matched filters
    • Gotta match 'em all: Solution diversification in graph matching matched filters
    • Multiplex graph matching matched filters
    • Shuffled total least squares
    • Unbiased estimation for additive exposure models
    • iGraphMatch
    • iGraphMatch : an R Package for the Analysis of Graph Matching
    • Causal Inference under Network Interference with Noise
    • Maximum likelihood estimation and graph matching in errorfully observed networks
    • Bias-Variance Tradeoffs in Joint Spectral Embeddings
    • Matchability of heterogeneous networks pairs
    • Matched Filters for Noisy Induced Subgraph Detection
    • Connectome Smoothing via Low-Rank Approximations
    • Graph Matching via Multi-Scale Heat Diffusion
    • Multiplex graph matching matched filters
    • Matched Filters for Noisy Induced Subgraph Detection
    • Metrics for Evaluating Network Alignment
    • Statistical Inference on Random Dot Product Graphs: a Survey
    • Tractable Graph Matching via Soft Seeding
    • What Is Connectome Coding?
    • A nonparametric two-sample hypothesis testing problem for random graphs
    • A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs
    • Adipose tissue measurement using magnetic resonance imaging: A survey
    • Elements of estimation theory for causal effects in the presence of network interference
    • A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs
    • Empirical Bayes estimation for the stochastic blockmodel
    • Analyzing statistical and computational tradeoffs of estimation procedures
    • Saturated Reconstruction of a Volume of Neocortex
    • Spectral clustering for divide-and-conquer graph matching
    • Statistical Inference on Errorfully Observed Graphs
    • Consistent latent position estimation and vertex classification for random dot product graphs
    • Foundations of adjacency spectral embedding
    • Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
    • Computing scalable multivariate glocal invariants of large (brain-) graphs
    • Consistent Adjacency-Spectral Partitioning for the Stochastic Block Model When the Model Parameters Are Unknown
    • Massive Diffusion MRI Graph Structure Preserves Spatial Information
    • Refinement of a Method for Identifying Probable Archaeological Sites from Remotely Sensed Data
    • Universally consistent vertex classification for latent positions graphs
    • A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs
    • Association between visceral adiposity and colorectal polyps on CT colonography
    • Refinement of a method for identifying probable archaeological sites from remotely sensed data
    • Fully automated adipose tissue measurement on abdominal CT
    • Automated fat measurement and segmentation with intensity inhomogeneity correction
    • Automated measurement and segmentation of abdominal adipose tissue in MRI
  • Projects
    • Example Project
  • Blog

iGraphMatch

Jan 1, 2021·
Zihuan Qiao
,
Daniel L Sussman
· 0 min read
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Last updated on Jan 1, 2021
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iGraphMatch : an R Package for the Analysis of Graph Matching Jan 1, 2021 →

© 2025 Daniel Sussman. This work is licensed under CC BY NC ND 4.0

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