26 citations · 52 across the 10 of their papers we have counts for
11 papers
Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs
Haoyu Han, Kai Guo, Harry Shomer +5
Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow t…
Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
Kai Guo, Xinnan Dai, Shenglai Zeng +4
Retrieval-augmented generation (RAG) is a powerful paradigm for improving large language models (LLMs) on knowledge-intensive question answering. Graph-based RAG (GraphRAG) leverag…
Higher-order Structure Boosts Link Prediction on Temporal Graphs
Jingzhe Liu, Zhigang Hua, Yan Xie +5
Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise…
RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
Haoyu Han, Li Ma, Yu Wang +9
Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks…
Sub-graph Based Diffusion Model for Link Prediction
Hang Li, Wei Jin, Geri Skenderi +4
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood…
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…
Mixture of Link Predictors on Graphs
Li Ma, Haoyu Han, Juanhui Li +4
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise m…
LPFormer: An Adaptive Graph Transformer for Link Prediction
Harry Shomer, Yao Ma, Haitao Mao +3
Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristi…
Revisiting Link Prediction: A Data Perspective
Haitao Mao, Juanhui Li, Harry Shomer +6
Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction. How…
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Juanhui Li, Harry Shomer, Haitao Mao +5
Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt…
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
Juanhui Li, Harry Shomer, Jiayuan Ding +5
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completio…