Bryan Perozzi | DeepAI

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∙ 05/26/2023

Unsupervised Embedding Quality Evaluation

Unsupervised learning has recently significantly gained in popularity, e...

0 Anton Tsitsulin, et al. ∙

∙ 07/14/2022

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

Personalized PageRank (PPR) is a fundamental tool in unsupervised learni...

0 Alessandro Epasto, et al. ∙

∙ 07/10/2022

Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks

A surge of interest in Graph Convolutional Networks (GCN) has produced t...

0 Minji Yoon, et al. ∙

∙ 05/20/2022

Tackling Provably Hard Representative Selection via Graph Neural Networks

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

0 Seyed Mehran Kazemi, et al. ∙

∙ 04/04/2022

Synthetic Graph Generation to Benchmark Graph Learning

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

0 Anton Tsitsulin, et al. ∙

∙ 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...

0 Minji Yoon, et al. ∙

∙ 02/28/2022

GraphWorld: Fake Graphs Bring Real Insights for GNNs

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

0 John Palowitch, et al. ∙

∙ 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...

0 Qi Zhu, et al. ∙

∙ 02/08/2021

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

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

11 Elan Markowitz, et al. ∙

∙ 10/24/2020

Pathfinder Discovery Networks for Neural Message Passing

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

0 Benedek Rozemberczki, et al. ∙

∙ 10/14/2020

InstantEmbedding: Efficient Local Node Representations

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

25 Ştefan Postăvaru, et al. ∙

∙ 07/23/2020

Grale: Designing Networks for Graph Learning

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

0 Jonathan Halcrow, et al. ∙

∙ 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 ...

0 Amol Kapoor, et al. ∙

∙ 07/03/2020

Scaling Graph Neural Networks with Approximate PageRank

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

0 Aleksandar Bojchevski, et al. ∙

∙ 06/30/2020

Graph Clustering with Graph Neural Networks

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

0 Anton Tsitsulin, et al. ∙

∙ 05/07/2020

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

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

44 Ines Chami, et al. ∙

∙ 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...

0 Anton Tsitsulin, et al. ∙

∙ 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...

0 John Palowitch, et al. ∙

∙ 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...

0 Alessandro Epasto, et al. ∙

∙ 04/30/2019

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

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

10 Sami Abu-El-Haija, et al. ∙

∙ 04/21/2019

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

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

0 Rami Al-Rfou, et al. ∙

∙ 02/24/2018

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

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

0 Sami Abu-El-Haija, et al. ∙

∙ 12/28/2017

ASYMP: Fault-tolerant Mining of Massive Graphs

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

0 Eduardo Fleury, et al. ∙

∙ 10/26/2017

Watch Your Step: Learning Graph Embeddings Through Attention

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

0 Sami Abu-El-Haija, et al. ∙

∙ 05/16/2017

Learning Edge Representations via Low-Rank Asymmetric Projections

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

0 Sami Abu-El-Haija, et al. ∙

∙ 05/12/2016

On the Convergent Properties of Word Embedding Methods

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

0 Yingtao Tian, et al. ∙

∙ 10/22/2015

Freshman or Fresher? Quantifying the Geographic Variation of Internet Language

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

0 Vivek Kulkarni, et al. ∙

∙ 01/15/2013

The Expressive Power of Word Embeddings

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

0 Yanqing Chen, et al. ∙