Search Results - Perozzi, Bryan

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  1. 1
    Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization

    Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization by Perozzi, Bryan, Akoglu, Leman

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  2. 2
    Vector-based similarity measurements for historical figures

    Vector-based similarity measurements for historical figures by Chen, Yanqing, Perozzi, Bryan, Skiena, Steven

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  3. 3
    HARP: Hierarchical Representation Learning for Networks

    HARP: Hierarchical Representation Learning for Networks by Chen, Haochen, Perozzi, Bryan, Hu, Yifan, Skiena, Steven

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  4. 4
    Debiasing Graph Representations via Metadata-Orthogonal Training

    Debiasing Graph Representations via Metadata-Orthogonal Training by Palowitch, John, Perozzi, Bryan

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  5. 5
    Scalable graph clustering with parallel approximate PageRank

    Scalable graph clustering with parallel approximate PageRank by Perozzi, Bryan, McCubbin, Christopher, Halbert, J. T.

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  6. 6
    The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure

    The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure by Tsitsulin, Anton, Perozzi, Bryan

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  7. 7
    Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

    Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts by Epasto, Alessandro, Perozzi, Bryan

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  8. 8
    HUGE: Huge Unsupervised Graph Embeddings with TPUs

    HUGE: Huge Unsupervised Graph Embeddings with TPUs by Mayer, Brandon, Tsitsulin, Anton, Fichtenberger, Hendrik, Halcrow, Jonathan, Perozzi, Bryan

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  9. 9
    GraphWorld: Fake Graphs Bring Real Insights for GNNs

    GraphWorld: Fake Graphs Bring Real Insights for GNNs by Palowitch, John, Tsitsulin, Anton, Mayer, Brandon, Perozzi, Bryan

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  10. 10
    Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs

    Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs by Tsitsulin, Anton, Munkhoeva, Marina, Perozzi, Bryan

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  11. 11
    Unsupervised Embedding Quality Evaluation

    Unsupervised Embedding Quality Evaluation by Tsitsulin, Anton, Munkhoeva, Marina, Perozzi, Bryan

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  12. 12
    MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit

    MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit by Palowitch, John, Perozzi, Bryan

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  13. 13
    DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

    DDGK: Learning Graph Representations for Deep Divergence Graph Kernels by Al-Rfou, Rami, Zelle, Dustin, Perozzi, Bryan

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  14. 14
    Grale: Designing Networks for Graph Learning

    Grale: Designing Networks for Graph Learning by Halcrow, Jonathan, Moşoi, Alexandru, Sam, Ruth, Perozzi, Bryan

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  15. 15
    Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

    Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank by Epasto, Alessandro, Mirrokni, Vahab, Perozzi, Bryan, Tsitsulin, Anton, Zhong, Peilin

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  16. 16
    Scaling Graph Neural Networks with Approximate PageRank

    Scaling Graph Neural Networks with Approximate PageRank by Bojchevski, Aleksandar, Gasteiger, Johannes, Perozzi, Bryan, Kapoor, Amol, Blais, Martin, Benedek Rózemberczki, Lukasik, Michal, Günnemann, Stephan

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  17. 17
    Graph Generative Model for Benchmarking Graph Neural Networks

    Graph Generative Model for Benchmarking Graph Neural Networks by Yoon, Minji, Wu, Yue, Palowitch, John, Perozzi, Bryan, Salakhutdinov, Ruslan

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  18. 18
    Explaining and Adapting Graph Conditional Shift

    Explaining and Adapting Graph Conditional Shift by Zhu, Qi, Jiao, Yizhu, Ponomareva, Natalia, Han, Jiawei, Perozzi, Bryan

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  19. 19
    Graph Clustering with Graph Neural Networks

    Graph Clustering with Graph Neural Networks by Tsitsulin, Anton, Palowitch, John, Perozzi, Bryan, Müller, Emmanuel

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  20. 20
    Best of Both Worlds: Advantages of Hybrid Graph Sequence Models

    Best of Both Worlds: Advantages of Hybrid Graph Sequence Models by Ali, Behrouz, Ali, Parviz, Karami, Mahdi, Clayton, Sanford, Perozzi, Bryan, Mirrokni, Vahab

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