World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
8562
World Ranking
9173
National Ranking
3903

Overview

Jennifer Neville is affiliated with Purdue University West Lafayette in the United States. Their research expertise lies primarily in computer science, with a focus on artificial intelligence, statistical and nonlinear physics, classics, information systems, and molecular biology.

The scientist has published extensively in the field of artificial intelligence and related areas, with particular emphasis on graph neural networks, topic modeling, natural language processing, complex network analysis, medieval literature and history, speech and dialogue systems, and semantic web and ontologies.

  • Topic Modeling
  • Advanced Graph Neural Networks
  • Natural Language Processing Techniques
  • Complex Network Analysis Techniques
  • Medieval Literature and History
  • Speech and dialogue systems
  • Semantic Web and Ontologies

Jennifer Neville has been actively publishing research articles in various venues, including:

  • arXiv (Cornell University)
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Neural Networks
  • Companion Proceedings of the Web Conference 2020
  • Computer

Among the recent papers authored or co-authored are:

  • "Adversarial Graph Augmentation to Improve Graph Contrastive Learning" (2021), published in arXiv (Cornell University)
  • "Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure" (2021), published in Proceedings of the International AAAI Conference on Web and Social Media
  • "Network Sampling Designs for Relational Classification" (2021), published in Proceedings of the International AAAI Conference on Web and Social Media
  • "Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies" (2023), published in arXiv (Cornell University)
  • "Dynamic Network Modeling from Motif-Activity" (2020), published in Companion Proceedings of the Web Conference 2020

Frequent collaborators include Mengting Wan, Tara Safavi, Longqi Yang, Megan Cavell, and Reid Andersen.

Best Publications

  • Modeling relationship strength in online social networks

    Rongjing Xiang;Jennifer Neville;Monica Rogati

  • Iterative Classification in Relational Data

    Jennifer Neville;David Jensen

  • Relational Dependency Networks

    Jennifer Neville;David Jensen

  • Why collective inference improves relational classification

    David Jensen;Jennifer Neville;Brian Gallagher

  • Structured comparative analysis of systems logs to diagnose performance problems

    Karthik Nagaraj;Charles Killian;Jennifer Neville

  • Learning relational probability trees

    Jennifer Neville;David Jensen;Lisa Friedland;Michael Hay

  • Using Transactional Information to Predict Link Strength in Online Social Networks

    Indika Kahanda;Jennifer Neville

  • Randomization tests for distinguishing social influence and homophily effects

    Timothy La Fond;Jennifer Neville

  • Efficient Graphlet Counting for Large Networks

    Nesreen K. Ahmed;Jennifer Neville;Ryan A. Rossi;Nick Duffield

  • Network Sampling: From Static to Streaming Graphs

    Nesreen K. Ahmed;Jennifer Neville;Ramana Kompella

  • Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning

    David Jensen;Jennifer Neville

  • Modeling dynamic behavior in large evolving graphs

    Ryan A. Rossi;Brian Gallagher;Jennifer Neville;Keith Henderson

  • ERACER: a database approach for statistical inference and data cleaning

    Chris Mayfield;Jennifer Neville;Sunil Prabhakar

  • Guided data repair

    Mohamed Yakout;Ahmed K. Elmagarmid;Jennifer Neville;Mourad Ouzzani

  • Leveraging relational autocorrelation with latent group models

    Jennifer Neville;David Jensen

  • Graph sample and hold: a framework for big-graph analytics

    Nesreen K. Ahmed;Nick Duffield;Jennifer Neville;Ramana Kompella

  • Using relational knowledge discovery to prevent securities fraud

    Jennifer Neville;Özgür Şimşek;David Jensen;John Komoroske

  • Simple estimators for relational Bayesian classifiers

    J. Neville;D. Jensen;B. Gallagher

  • Temporal-Relational Classifiers for Prediction in Evolving Domains

    U. Sharan;J. Neville

  • Attributed graph models: modeling network structure with correlated attributes

    Joseph J. Pfeiffer;Sebastian Moreno;Timothy La Fond;Jennifer Neville

  • Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

    Paul N. Bennett;Vanja Josifovski;Jennifer Neville;Filip Radlinski

Frequent Co-Authors

David Jensen
David Jensen University of Massachusetts Amherst
Nesreen K. Ahmed
Nesreen K. Ahmed Intel (United States)
Paul N. Bennett
Paul N. Bennett Microsoft (United States)
Nick Duffield
Nick Duffield Texas A&M University
Sunil Prabhakar
Sunil Prabhakar Purdue University West Lafayette
Ahmed K. Elmagarmid
Ahmed K. Elmagarmid Qatar Computing Research Institute
Gabriel Ghinita
Gabriel Ghinita Hamad bin Khalifa University
Mourad Ouzzani
Mourad Ouzzani Qatar Computing Research Institute
Tina Eliassi-Rad
Tina Eliassi-Rad Northeastern University

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