8 papers
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…
Automated Label Placement on Maps via Large Language Models
Harry Shomer, Jiejun Xu
Label placement is a critical aspect of map design, serving as a form of spatial annotation that directly impacts clarity and interpretability. Despite its importance, label placem…
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…
Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Harry Shomer, Jay Revolinsky, Jiliang Tang
Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG). Recently, there's been an increased focus on designing KGC methods that can excel in t…
A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimization
Yucheng Chu, Hang Li, Kaiqi Yang +4
Open-ended short-answer questions (SAGs) have been widely recognized as a powerful tool for providing deeper insights into learners' responses in the context of learning analytics…