600 citations · 668 across the 14 of their papers we have counts for
10 papers · 1 filter
Graph-level Neural Networks: Current Progress and Future Directions
Ge Zhang, Jia Wu, Jian Yang +6
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of…
Towards Unsupervised Deep Graph Structure Learning
Yixin Liu, Yu Zheng, Daokun Zhang +3
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated whe…
A Robust and Generalized Framework for Adversarial Graph Embedding
Jianxin Li, Xingcheng Fu, Hao Peng +5
Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-qua…
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Gongxu Luo, Jianxin Li, Jianlin Su +5
Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate…
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Jianxin Li, Hao Peng, Yuwei Cao +4
Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis ta…
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
Li Sun, Zhongbao Zhang, Jiawei Zhang +4
Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three fol…