3 citations · 6 across the 13 of their papers we have counts for
12 papers · 1 filter
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets c…
Knowledge-aware contrastive heterogeneous molecular graph learning
Mukun Chen, Jia Wu, Shirui Pan +4
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph…
DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
Zelin Yao, Chuang Liu, Xianke Meng +4
Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. In real-world scenarios, graph data within the same dataset can vary significantly in scal…
Dual-perspective Cross Contrastive Learning in Graph Transformers
Zelin Yao, Chuang Liu, Xueqi Ma +5
Graph contrastive learning (GCL) is a popular method for leaning graph representations by maximizing the consistency of features across augmented views. Traditional GCL methods uti…
Hi-GMAE: Hierarchical Graph Masked Autoencoders
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node…
Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
Chuang Liu, Yuyao Wang, Yibing Zhan +4
Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a strai…
Gradformer: Graph Transformer with Exponential Decay
Chuang Liu, Zelin Yao, Yibing Zhan +3
Graph Transformers (GTs) have demonstrated their advantages across a wide range of tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases, parti…
Exploring Sparsity in Graph Transformers
Chuang Liu, Yibing Zhan, Xueqi Ma +5
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, espe…
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes
Chuang Liu, Wenhang Yu, Kuang Gao +5
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods…
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
Chuang Liu, Yibing Zhan, Baosheng Yu +4
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current…
State of the Art and Potentialities of Graph-level Learning
Zhenyu Yang, Ge Zhang, Jia Wu +9
Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as inp…
CLNode: Curriculum Learning for Node Classification
Xiaowen Wei, Xiuwen Gong, Yibing Zhan +3
Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods.…