270 citations · 805 across the 13 of their papers we have counts for
4 papers · 1 filter
MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit
John Palowitch, Bryan Perozzi
Are Graph Neural Networks (GNNs) fair? In many real world graphs, the formation of edges is related to certain node attributes (e.g. gender, community, reputation). In this case, s…
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…