Title
Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning.
Abstract
Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed.
Year
DOI
Venue
2019
10.24963/ijcai.2019/267
IJCAI
Field
DocType
ISSN
Graph,Semi-supervised learning,Computer science,Heat kernel,Artificial intelligence,Machine learning
Conference
IJCAI2019
Citations 
PageRank 
References 
4
0.39
0
Authors
5
Name
Order
Citations
PageRank
Bingbing Xu1113.98
Huawei Shen273961.40
Qi Cao3415.38
Keting Cen4292.81
Xueqi Cheng53148247.04