20 citations · 21 across the 3 of their papers we have counts for
5 papers
LinkGPT: Teaching Large Language Models To Predict Missing Links
Zhongmou He, Jing Zhu, Shengyi Qian +2
Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, par…
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
Jing Zhu, Xiang Song, Vassilis N. Ioannidis +2
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks? Graph Neural Networks (GNNs) have become the state-of-the…
Touch and Go: Learning from Human-Collected Vision and Touch
Fengyu Yang, Chenyang Ma, Jiacheng Zhang +3
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world. We propose a dataset with paired visual and tactile…
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
Jing Zhu, Xingyu Lu, Mark Heimann +1
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node ro…
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases
Tara Safavi, Jing Zhu, Danai Koutra
Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence. Recently, much progress toward this goal has been made with automatic knowledge base…