44 citations · 73 across the 18 of their papers we have counts for
5 papers · 1 filter
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
Zhuo Xu, Lu Bai, Lixin Cui +3
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective…
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
Zhehan Zhao, Lu Bai, Lixin Cui +4
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from t…
Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System
Jing Li, Lu Bai, Bin Yang +4
Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the…
SSHPool: The Separated Subgraph-based Hierarchical Pooling
Zhuo Xu, Lixin Cui, Ming Li +6
In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning…
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
Feifei Qian, Lixin Cui, Ming Li +6
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are d…