
Pathfinder Discovery Networks for Neural Message Passing
In this work we propose Pathfinder Discovery Networks (PDNs), a method f...
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InstantEmbedding: Efficient Local Node Representations
In this paper, we introduce InstantEmbedding, an efficient method for ge...
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Grale: Designing Networks for Graph Learning
How can we find the right graph for semisupervised learning? In real wo...
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Examining COVID19 Forecasting using SpatioTemporal Graph Neural Networks
In this work, we examine a novel forecasting approach for COVID19 case ...
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Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
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Graph Clustering with Graph Neural Networks
Graph Neural Networks (GNNs) have achieved stateoftheart results on m...
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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...
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Just SLaQ When You Approximate: Accurate Spectral Distances for WebScale Graphs
Graph comparison is a fundamental operation in data mining and informati...
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MONET: Debiasing Graph Embeddings via the MetadataOrthogonal Training Unit
Are Graph Neural Networks (GNNs) fair? In many real world graphs, the fo...
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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...
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MixHop: HigherOrder Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semisupervised learning with Graph Neural ...
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DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Can neural networks learn to compare graphs without feature engineering?...
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NGCN: Multiscale Graph Convolution for Semisupervised Node Classification
Graph Convolutional Networks (GCNs) have shown significant improvements ...
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ASYMP: Faulttolerant Mining of Massive Graphs
We present ASYMP, a distributed graph processing system developed for th...
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Watch Your Step: Learning Graph Embeddings Through Attention
Graph embedding methods represent nodes in a continuous vector space, pr...
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Learning Edge Representations via LowRank Asymmetric Projections
We propose a new method for embedding graphs while preserving directed e...
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On the Convergent Properties of Word Embedding Methods
Do word embeddings converge to learn similar things over different initi...
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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...
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The Expressive Power of Word Embeddings
We seek to better understand the difference in quality of the several pu...
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Bryan Perozzi
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