World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8089
World Ranking
10070
National Ranking
4248

Overview

David F. Gleich is affiliated with Purdue University West Lafayette in the United States. Their research primarily spans the fields of Computer Science and Physics and Astronomy, with a significant focus on Statistical and Nonlinear Physics, Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, and Computer Networks and Communications.

The scientist's work addresses diverse topics including Complex Network Analysis Techniques, Advanced Graph Neural Networks, Topological and Geometric Data Analysis, Opinion Dynamics and Social Influence, Sparse and Compressive Sensing Techniques, Tensor Decomposition and Applications, and studies related to COVID-19 epidemiology.

Among their recent papers are:

  • Dimensionality of Social Networks Using Motifs and Eigenvalues, 2021, PLoS ONE
  • Neighborhood and PageRank methods for pairwise link prediction, 2020, Social Network Analysis and Mining
  • Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data, 2021, arXiv (Cornell University)
  • Estimating statewide carrying capacity of bobcats (Lynx rufus) using improved maximum clique algorithms, 2022, Landscape Ecology

Frequent co-authors collaborating with David F. Gleich include:

  • Nate Veldt
  • Austin R. Benson
  • Huda Nassar
  • Yufan Huang
  • Meng Liu

The scientist's work has appeared in multiple publication venues, with recurrent contributions to:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • PLoS ONE
  • Social Network Analysis and Mining
  • Landscape Ecology

David F. Gleich has contributed to academic literature through book publication with Springer Science+Business Media, authoring the book titled Algorithms and Models for the Web Graph in 2020.

Best Publications

  • Higher-order organization of complex networks

    Austin R. Benson;David F. Gleich;Jure Leskovec

  • PageRank Beyond the Web

    David F. Gleich

  • Local Higher-Order Graph Clustering

    Hao Yin;Austin R. Benson;Jure Leskovec;David F. Gleich

  • Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

    Joyce Jiyoung Whang;David F. Gleich;Inderjit S. Dhillon

  • Heat kernel based community detection

    Kyle Kloster;David F. Gleich

  • Overlapping community detection using seed set expansion

    Joyce Jiyoung Whang;David F. Gleich;Inderjit S. Dhillon

  • Vertex neighborhoods, low conductance cuts, and good seeds for local community methods

    David F. Gleich;C. Seshadhri

  • Rank aggregation via nuclear norm minimization

    David F. Gleich;Lek-heng Lim

  • Message-Passing Algorithms for Sparse Network Alignment

    Mohsen Bayati;David F. Gleich;Amin Saberi;Ying Wang

  • Algorithms for Large, Sparse Network Alignment Problems

    Mohsen Bayati;Margot Gerritsen;David F. Gleich;Amin Saberi

  • Fast Parallel PageRank: A Linear System Approach

    David Gleich;Leonid Zhukov;Pavel Berkhin

  • Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

    Austin R Benson;David F Gleich;Jure Leskovec

  • Parallel Maximum Clique Algorithms with Applications to Network Analysis

    Ryan A. Rossi;David F. Gleich;Assefaw Hadish Gebremedhin

  • Fast maximum clique algorithms for large graphs

    Ryan A. Rossi;David F. Gleich;Assefaw H. Gebremedhin;Md. Mostofa Ali Patwary

  • An Inner-Outer Iteration for Computing PageRank

    David F. Gleich;Andrew P. Gray;Chen Greif;Tracy Lau

  • Multilinear PageRank

    David F. Gleich;Lek-Heng Lim;Yongyang Yu

  • Non-exhaustive, Overlapping k -means.

    Joyce Jiyoung Whang;Inderjit S. Dhillon;David F. Gleich

  • Using Triangles to Improve Community Detection in Directed Networks

    Christine Klymko;David F. Gleich;Tamara G. Kolda

  • Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures

    Austin R. Benson;David F. Gleich;James Demmel

  • Tall and skinny QR factorizations in MapReduce architectures

    Paul G. Constantine;David F. Gleich

Frequent Co-Authors

Ananth Grama
Ananth Grama Purdue University West Lafayette
Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)
Amin Saberi
Amin Saberi Stanford University
Michael W. Mahoney
Michael W. Mahoney University of California, Berkeley
Francesco Bonchi
Francesco Bonchi Institute for Scientific Interchange
Laks V. S. Lakshmanan
Laks V. S. Lakshmanan University of British Columbia
Jure Leskovec
Jure Leskovec Stanford University
Karthik Ramani
Karthik Ramani Purdue University West Lafayette
Michael Gribskov
Michael Gribskov Purdue University West Lafayette
Jason D. Lee
Jason D. Lee Princeton University

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