.png){kind=icon}
Return
Quantum-based subgraph convolutional neural networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2018.11.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks, which we refer to as the quantum-based subgraph convolutional neural network (QS-CNNs). This new architecture captures both the global topological structure and the local connectivity structure within a graph. Specifically, we commence by establishing a family of K-layer expansion subgraphs for each vertex of a graph by quantum walks, which captures the global topological arrangement information for substructures contained within a graph. We then design a set of fixed-size convolution filters over the subgraphs, which helps to characterise multi scale patterns residing in the data. The idea is to apply convolution filters sliding over the entire set of subgraphs rooted at a vertex to extract the local features analogous to the standard convolution operation on grid data. Experiments on eight graph-structured datasets demonstrate that QS-CNNs architecture is capable of outperforming fourteen state-of-the-art methods for the tasks of node classification and graph classification. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Graph convolutional neural networks
Spatial construction
Quantum walks
Subgraph
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
View more organizations
Cited Papers
ShareSaveβ-VLDL protects against Aβ(1-42) and apoE toxicity in human SH-SY5Y neuroblastoma cells.png)
Neuroreport
NeuroreportIF0
PREAI
ShareSaveShareSaveEnvironmental Factors and Seasonality Affect the Concentration of Rotundone in Vitis vinifera L. cv. Shiraz WinePLOS ONEOAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave View more