270 citations · 484 across the 6 of their papers we have counts for
6 papers
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor +5
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mix…
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
Alessandro Epasto, Bryan Perozzi
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector rep…
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Rami Al-Rfou, Dustin Zelle, Bryan Perozzi
Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domai…
Statistically Significant Detection of Linguistic Change
Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi +1
We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especia…
POLYGLOT-NER: Massive Multilingual Named Entity Recognition
Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi +1
The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is wr…
Exploring the power of GPU's for training Polyglot language models
Vivek Kulkarni, Rami Al-Rfou', Bryan Perozzi +1
One of the major research trends currently is the evolution of heterogeneous parallel computing. GP-GPU computing is being widely used and several applications have been designed t…