5 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…
From Sequence to Structure: Uncovering Substructure Reasoning in Transformers
Xinnan Dai, Kai Yang, Jay Revolinsky +4
Recent studies suggest that large language models (LLMs) possess the capability to solve graph reasoning tasks. Notably, even when graph structures are embedded within textual desc…
Towards Understanding Link Predictor Generalizability Under Distribution Shifts
Jay Revolinsky, Harry Shomer, Jiliang Tang
State-of-the-art link prediction (LP) models demonstrate impressive benchmark results. However, popular benchmark datasets often assume that training, validation, and testing sampl…
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…