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07/26/2023
HUGE: Huge Unsupervised Graph Embeddings with TPUs
Graphs are a representation of structured data that captures the relatio...
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07/17/2023
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
Despite a surge in interest in GNN development, homogeneity in benchmark...
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06/05/2023
Explaining and Adapting Graph Conditional Shift
Graph Neural Networks (GNNs) have shown remarkable performance on graph-...
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05/26/2023
Unsupervised Embedding Quality Evaluation
Unsupervised learning has recently significantly gained in popularity, e...
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07/14/2022
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Personalized PageRank (PPR) is a fundamental tool in unsupervised learni...
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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...
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05/20/2022
Tackling Provably Hard Representative Selection via Graph Neural Networks
Representative selection (RS) is the problem of finding a small subset o...
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04/04/2022
Synthetic Graph Generation to Benchmark Graph Learning
Graph learning algorithms have attained state-of-the-art performance on ...
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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...
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02/28/2022
GraphWorld: Fake Graphs Bring Real Insights for GNNs
Despite advances in the field of Graph Neural Networks (GNNs), only a sm...
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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...
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02/08/2021
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Graph Representation Learning (GRL) methods have impacted fields from ch...
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10/24/2020
Pathfinder Discovery Networks for Neural Message Passing
In this work we propose Pathfinder Discovery Networks (PDNs), a method f...
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10/14/2020
InstantEmbedding: Efficient Local Node Representations
In this paper, we introduce InstantEmbedding, an efficient method for ge...
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07/23/2020
Grale: Designing Networks for Graph Learning
How can we find the right graph for semi-supervised learning? In real wo...
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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 ...
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07/03/2020
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
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06/30/2020
Graph Clustering with Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results on m...
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05/07/2020
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations fo...
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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...
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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...
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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...
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04/30/2019
MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural ...
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04/21/2019
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Can neural networks learn to compare graphs without feature engineering?...
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02/24/2018
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Graph Convolutional Networks (GCNs) have shown significant improvements ...
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12/28/2017
ASYMP: Fault-tolerant Mining of Massive Graphs
We present ASYMP, a distributed graph processing system developed for th...
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10/26/2017
Watch Your Step: Learning Graph Embeddings Through Attention
Graph embedding methods represent nodes in a continuous vector space, pr...
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05/16/2017
Learning Edge Representations via Low-Rank Asymmetric Projections
We propose a new method for embedding graphs while preserving directed e...
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05/12/2016
On the Convergent Properties of Word Embedding Methods
Do word embeddings converge to learn similar things over different initi...
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10/22/2015
Freshman or Fresher? Quantifying the Geographic Variation of Internet Language
We present a new computational technique to detect and analyze statistic...
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01/15/2013
