Sami Abu-El-Haija

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 …

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 …