27 citations · 57 across the 13 of their papers we have counts for
6 papers · 1 filter
Local Message-Passing for Discrete Graph Generation
Jay Revolinsky, Harry Shomer, Jiliang Tang
Discrete graph generation has emerged as a powerful paradigm for modeling graph-structured data, yet state of the art models often rely on Graph Transformers or higher order archit…
A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation
Yu Song, Zhigang Hua, Harry Shomer +4
Link Prediction (LP) is a critical task in graph machine learning. While Graph Neural Networks (GNNs) have significantly advanced LP performance recently, existing methods face key…
Subgraph Generation for Generalizing on Out-of-Distribution Links
Jay Revolinsky, Harry Shomer, Jiliang Tang
Graphs Neural Networks (GNNs) demonstrate high-performance on the link prediction (LP) task. However, these models often rely on all dataset samples being drawn from the same distr…
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
Harry Shomer, Yao Ma, Juanhui Li +3
Knowledge graph completion (KGC) aims to predict unseen edges in knowledge graphs (KGs), resulting in the discovery of new facts. A new class of methods have been proposed to tackl…
Toward Degree Bias in Embedding-Based Knowledge Graph Completion
Harry Shomer, Wei Jin, Wentao Wang +1
A fundamental task for knowledge graphs (KGs) is knowledge graph completion (KGC). It aims to predict unseen edges by learning representations for all the entities and relations in…
Learning Representations for Hyper-Relational Knowledge Graphs
Harry Shomer, Wei Jin, Juanhui Li +2
Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts…