Chong Wu
Department of Electrical Engineering and Centre for Intelligent Multidimensional Data Analysis, Department of Electrical Engineering, Department of Electrical Engineering, Department of Electrical Engineering, Department of Electrical Engineering, Department of Electrical Engineering
Corresponding Author:[email protected]
Author ProfileAbstract
We present a novel graph convolutional method called star topology
convolution (STC). This method makes graph convolution more similar to
conventional convolutional neural networks (CNNs) in Euclidean feature
spaces. STC learns subgraphs which have a star topology rather than
learning a fixed graph like most spectral methods. Due to the properties
of a star topology, STC is graph-scale free (without a fixed graph size
constraint). It has fewer parameters in its convolutional filter and is
inductive, so it is more flexible and can be applied to large and
evolving graphs. The convolutional filter is learnable and localized,
similar to CNNs in Euclidean feature spaces, and can share weights
across graphs. To test the method, STC was compared with
state-of-the-art graph convolutional methods in a supervised learning
setting on nine node properties prediction benchmark datasets: Cora,
Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental
results showed that STC achieved state-of-the-art performance on all
these datasets and maintained good robustness. In an essential protein
identification task, STC outperformed state-of-the-art essential protein
identification methods. An application of using pretrained STC as the
embedding for feature extraction of some downstream classification tasks
was introduced. The experimental results showed that STC can share
weights across different graphs and be used as the embedding to improve
the performance of downstream tasks.