Paper Info
Title
Multi-Scale Aggregation Graph Neural Networks Based On Feature Similarity For Semi-Supervised Learning
Abstract
The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.
Year
DOI
Venue
2021
10.3390/e23040403
ENTROPY
Keywords
DocType
Volume
graph analysis, graph neural network, semi-supervised learning, neighborhood aggregation
Journal
23
Issue
ISSN
Citations 
4
1099-4300
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Xun Zhang102.03
Lanyan Yang200.34
Bin Zhang300.34
ying liu436446.92
Dong Jiang552.78
Xiaohai Qin600.34
Mengmeng Hao700.68