b'Bryan Perozzi'

research

∙ 05/20/2022

Tackling Provably Hard Representative Selection via Graph Neural Networks

Representative selection (RS) is the problem of finding a small subset o...

Seyed Mehran Kazemi, et al. ∙

research

∙ 04/04/2022

Synthetic Graph Generation to Benchmark Graph Learning

Graph learning algorithms have attained state-of-the-art performance on ...

Anton Tsitsulin, et al. ∙

research

∙ 03/03/2022

Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks

How can we make predictions for nodes in a heterogeneous graph when an e...

Minji Yoon, et al. ∙

research

∙ 02/28/2022

GraphWorld: Fake Graphs Bring Real Insights for GNNs

Despite advances in the field of Graph Neural Networks (GNNs), only a sm...

John Palowitch, et al. ∙

research

∙ 08/02/2021

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data

There has been a recent surge of interest in designing Graph Neural Netw...

Qi Zhu, et al. ∙

research

∙ 02/08/2021

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

Graph Representation Learning (GRL) methods have impacted fields from ch...

Elan Markowitz, et al. ∙

research

∙ 10/24/2020

Pathfinder Discovery Networks for Neural Message Passing

In this work we propose Pathfinder Discovery Networks (PDNs), a method f...

Benedek Rozemberczki, et al. ∙

research

∙ 10/14/2020

InstantEmbedding: Efficient Local Node Representations

In this paper, we introduce InstantEmbedding, an efficient method for ge...

Ştefan Postăvaru, et al. ∙

research

∙ 07/23/2020

Grale: Designing Networks for Graph Learning

How can we find the right graph for semi-supervised learning? In real wo...

Jonathan Halcrow, et al. ∙

research

∙ 07/06/2020

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

In this work, we examine a novel forecasting approach for COVID-19 case ...

Amol Kapoor, et al. ∙

research

∙ 07/03/2020

Scaling Graph Neural Networks with Approximate PageRank

Graph neural networks (GNNs) have emerged as a powerful approach for sol...

Aleksandar Bojchevski, et al. ∙

research

∙ 06/30/2020

Graph Clustering with Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art results on m...

Anton Tsitsulin, et al. ∙

research

∙ 05/07/2020

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

There has been a surge of recent interest in learning representations fo...

Ines Chami, et al. ∙

research

∙ 03/03/2020

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

Graph comparison is a fundamental operation in data mining and informati...

Anton Tsitsulin, et al. ∙

research

∙ 09/25/2019

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

Are Graph Neural Networks (GNNs) fair? In many real world graphs, the fo...

John Palowitch, et al. ∙

research

∙ 05/06/2019

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...

Alessandro Epasto, et al. ∙

research

∙ 04/30/2019

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

Existing popular methods for semi-supervised learning with Graph Neural ...

Sami Abu-El-Haija, et al. ∙

research

∙ 04/21/2019

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

Can neural networks learn to compare graphs without feature engineering?...

Rami Al-Rfou, et al. ∙

research

∙ 02/24/2018

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

Graph Convolutional Networks (GCNs) have shown significant improvements ...

Sami Abu-El-Haija, et al. ∙

research

∙ 12/28/2017

ASYMP: Fault-tolerant Mining of Massive Graphs

We present ASYMP, a distributed graph processing system developed for th...

Eduardo Fleury, et al. ∙

research

∙ 10/26/2017

Watch Your Step: Learning Graph Embeddings Through Attention

Graph embedding methods represent nodes in a continuous vector space, pr...

Sami Abu-El-Haija, et al. ∙

research

∙ 05/16/2017

Learning Edge Representations via Low-Rank Asymmetric Projections

We propose a new method for embedding graphs while preserving directed e...

Sami Abu-El-Haija, et al. ∙

research

∙ 05/12/2016

On the Convergent Properties of Word Embedding Methods

Do word embeddings converge to learn similar things over different initi...

Yingtao Tian, et al. ∙

research

∙ 10/22/2015

Freshman or Fresher? Quantifying the Geographic Variation of Internet Language

We present a new computational technique to detect and analyze statistic...

Vivek Kulkarni, et al. ∙

research

∙ 01/15/2013

The Expressive Power of Word Embeddings

We seek to better understand the difference in quality of the several pu...

Yanqing Chen, et al. ∙

Bryan Perozzi

Featured Co-authors

Ruslan Salakhutdinov

Jiawei Han

Christopher Ré

Stephan Günnemann

Aram Galstyan

Kevin Murphy

Vahab Mirrokni

Kristina Lerman

Qi Zhu

Greg Ver Steeg

Silvio Lattanzi

Tackling Provably Hard Representative Selection via Graph Neural Networks

Synthetic Graph Generation to Benchmark Graph Learning

Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks

GraphWorld: Fake Graphs Bring Real Insights for GNNs

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

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