Bryan Perozzi

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Association for Computing Machinery ∙ Stony Brook University

Pathfinder Discovery Networks for Neural Message Passing

In this work we propose Pathfinder Discovery Networks (PDNs), a method f...
10/24/2020 ∙ by Benedek Rozemberczki, et al. ∙ 0 ∙ share

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InstantEmbedding: Efficient Local Node Representations

In this paper, we introduce InstantEmbedding, an efficient method for ge...
10/14/2020 ∙ by Ştefan Postăvaru, et al. ∙ 25 ∙ share

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Grale: Designing Networks for Graph Learning

How can we find the right graph for semi-supervised learning? In real wo...
07/23/2020 ∙ by Jonathan Halcrow, et al. ∙ 0 ∙ share

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Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

In this work, we examine a novel forecasting approach for COVID-19 case ...
07/06/2020 ∙ by Amol Kapoor, et al. ∙ 0 ∙ share

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Scaling Graph Neural Networks with Approximate PageRank

Graph neural networks (GNNs) have emerged as a powerful approach for sol...
07/03/2020 ∙ by Aleksandar Bojchevski, et al. ∙ 0 ∙ share

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Graph Clustering with Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art results on m...
06/30/2020 ∙ by Anton Tsitsulin, et al. ∙ 0 ∙ share

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Machine Learning on Graphs: A Model and Comprehensive Taxonomy

There has been a surge of recent interest in learning representations fo...
05/07/2020 ∙ by Ines Chami, et al. ∙ 44 ∙ share

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

Graph comparison is a fundamental operation in data mining and informati...
03/03/2020 ∙ by Anton Tsitsulin, et al. ∙ 0 ∙ share

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

Are Graph Neural Networks (GNNs) fair? In many real world graphs, the fo...
09/25/2019 ∙ by John Palowitch, et al. ∙ 0 ∙ share

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

Recent interest in graph embedding methods has focused on learning a sin...
05/06/2019 ∙ by Alessandro Epasto, et al. ∙ 0 ∙ share

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MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing

Existing popular methods for semi-supervised learning with Graph Neural ...
04/30/2019 ∙ by Sami Abu-El-Haija, et al. ∙ 10 ∙ share

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DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

Can neural networks learn to compare graphs without feature engineering?...
04/21/2019 ∙ by Rami Al-Rfou, et al. ∙ 0 ∙ share

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N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

Graph Convolutional Networks (GCNs) have shown significant improvements ...
02/24/2018 ∙ by Sami Abu-El-Haija, et al. ∙ 0 ∙ share

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ASYMP: Fault-tolerant Mining of Massive Graphs

We present ASYMP, a distributed graph processing system developed for th...
12/28/2017 ∙ by Eduardo Fleury, et al. ∙ 0 ∙ share

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Watch Your Step: Learning Graph Embeddings Through Attention

Graph embedding methods represent nodes in a continuous vector space, pr...
10/26/2017 ∙ by Sami Abu-El-Haija, et al. ∙ 0 ∙ share

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Learning Edge Representations via Low-Rank Asymmetric Projections

We propose a new method for embedding graphs while preserving directed e...
05/16/2017 ∙ by Sami Abu-El-Haija, et al. ∙ 0 ∙ share

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On the Convergent Properties of Word Embedding Methods

Do word embeddings converge to learn similar things over different initi...
05/12/2016 ∙ by Yingtao Tian, et al. ∙ 0 ∙ share

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Freshman or Fresher? Quantifying the Geographic Variation of Internet Language

We present a new computational technique to detect and analyze statistic...
10/22/2015 ∙ by Vivek Kulkarni, et al. ∙ 0 ∙ share

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The Expressive Power of Word Embeddings

We seek to better understand the difference in quality of the several pu...
01/15/2013 ∙ by Yanqing Chen, et al. ∙ 0 ∙ share

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Bryan Perozzi

Featured Co-authors

Christopher Ré

Kevin Murphy

Aram Galstyan

Greg Ver Steeg

Kristina Lerman

Vahab Mirrokni

Stephan Günnemann

Steven Skiena

Vivek Kulkarni

Silvio Lattanzi

Rami Al-Rfou

Pathfinder Discovery Networks for Neural Message Passing

InstantEmbedding: Efficient Local Node Representations

Grale: Designing Networks for Graph Learning

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

Scaling Graph Neural Networks with Approximate PageRank

Graph Clustering with Graph Neural Networks

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

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

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

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

MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

ASYMP: Fault-tolerant Mining of Massive Graphs

Watch Your Step: Learning Graph Embeddings Through Attention

Learning Edge Representations via Low-Rank Asymmetric Projections

On the Convergent Properties of Word Embedding Methods

Freshman or Fresher? Quantifying the Geographic Variation of Internet Language

The Expressive Power of Word Embeddings