866 citations · 1.1k across the 11 of their papers we have counts for
9 papers · 1 filter
When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected
Haotian Xu, Yuning You, Tengfei Ma
Graphs provide a unified representation of semantic content and relational structure, making them a natural fit for domains such as molecular modeling, citation networks, and socia…
Correlational Lagrangian Schrödinger Bridge: Learning Dynamics with Population-Level Regularization
Yuning You, Ruida Zhou, Yang Shen
Accurate modeling of system dynamics holds intriguing potential in broad scientific fields including cytodynamics and fluid mechanics. This task often presents significant challeng…
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
Haotao Wang, Ziyu Jiang, Yuning You +5
Graph neural networks (GNNs) have found extensive applications in learning from graph data. However, real-world graphs often possess diverse structures and comprise nodes and edges…
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei, Yuning You, Tianlong Chen +3
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (w…
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Yuning You, Tianlong Chen, Zhangyang Wang +1
Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on doma…
Graph Contrastive Learning Automated
Yuning You, Tianlong Chen, Yang Shen +1
Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, g…
Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui +3
Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been develop…
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You, Tianlong Chen, Zhangyang Wang +1
Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning…
L-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Yuning You, Tianlong Chen, Zhangyang Wang +1
Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets. They need to compute node representa…