20 citations · 45 across the 7 of their papers we have counts for
7 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…
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis +4
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including th…
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…
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Jing Zhu, Danai Koutra, Mark Heimann
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel…
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…