Bryan Perozzi

Watch Your Step: Learning Node Embeddings via Graph Attention

2017 · arXiv (Cornell University)

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to …

Pathfinder Discovery Networks for Neural Message Passing

2021

In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model. PDNs inductively learn an aggregated weight for each …

UGSL: A Unified Framework for Benchmarking Graph Structure Learning

2023 · arXiv (Cornell University)

Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, some recent methods substantially expanded the applicability of GNNs …

Statistically Significant Detection of Linguistic Change

2015

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 especially prevalent on the Internet, where the rapid exchange of …