44 citations · 73 across the 18 of their papers we have counts for
18 papers
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…
Graph Representation Learning for Infrared and Visible Image Fusion
Jing Li, Lu Bai, Bin Yang +3
Infrared and visible image fusion aims to extract complementary features to synthesize a single fused image. Many methods employ convolutional neural networks (CNNs) to extract loc…
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
Ahmed Begga, Francisco Escolano, Miguel Angel Lozano +1
High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the…
Labeled Subgraph Entropy Kernel
Chengyu Sun, Xing Ai, Zhihong Zhang +1
In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial da…
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
Lixin Cui, Ming Li, Yue Wang +2
In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph str…
Quantum Graph Learning: Frontiers and Outlook
Shuo Yu, Ciyuan Peng, Yingbo Wang +3
Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates t…
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…
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification
Lu Bai, Lixin Cui, Edwin R. Hancock
In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification. To this end, we commence by computing the Average…
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification
Lu Bai, Lixin Cui, Yue Wang +2
In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs. Different from most e…
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
Yue Wang, Yao Wan, Lu Bai +5
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples cont…
Two-level Graph Neural Network
Xing Ai, Chengyu Sun, Zhihong Zhang +1
Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, exist…
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach
Xing Ai, Zhihong Zhang, Luzhe Sun +2
Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an eff…
Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
Yuqing Ma, Xianglong Liu, Shihao Bai +4
Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional arch…
Graph Convolutional Neural Networks based on Quantum Vertex Saliency
Lu Bai, Yuhang Jiao, Luca Rossi +3
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike…