44 citations · 73 across the 18 of their papers we have counts for
5 papers · 1 filter
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…