.png){kind=icon}
返回
Data Representation via Attribute Selection- Propagation Neural Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TSIPN.2023.3266949.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph Convolutional Networks (GCNs) have been commonly studied for attribute graph data representation. It is known that the core of Graph Convolution (GC) operation is to define a specific graph propagation operation for graph node's attributes. Existing GCs mainly perform propagation over node's all attributes. However, this 'full' propagation may be unreasonable on some practical learning tasks and also be vulnerable to the noisy attributes. To address this issue, in this article, we propose a novel Attribute Selection-Propagation (ASP) mechanism for attribute graph data representation by incorporating attribute selection into GCs. The main aspect of the proposed ASP is that it can be formulated as a regularization model based on which we can derive a simple update rule to implement ASP in a self-supervised manner. ASP aims to adaptively propagate some optimal attributes of node to better serve message passing in GCs. Using ASP, we then present a novel graph neural network, named ASPNet for attribute graph representation and learning. Experiments on several graph learning tasks including node classification, clustering and link prediction demonstrate the effectiveness of the proposed ASPNet.
Keyword:
Convolution
Task analysis
Convolutional neural networks
Noise measurement
Information processing
Graph neural networks
Social networking (online)
Graph convolutional networks
full propagation
attribute selection-propagation
graph learning tasks
期刊
IF:
4.9
论文数:
736
被引数:
1.9K
机构
引用论文
Accelerometer-assessed sedentary work, leisure time and cardio-metabolic biomarkers during one year: Effectiveness of a cluster randomized controlled trial in parents with a sedentary occupation and young children.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏分享收藏
查看更多内容