1 research outputs found
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
Data augmentation aims to generate new and synthetic features from the
original data, which can identify a better representation of data and improve
the performance and generalizability of downstream tasks. However, data
augmentation for graph-based models remains a challenging problem, as graph
data is more complex than traditional data, which consists of two features with
different properties: graph topology and node attributes. In this paper, we
study the problem of graph data augmentation for Graph Convolutional Network
(GCN) in the context of improving the node embeddings for semi-supervised node
classification. Specifically, we conduct cosine similarity based cross
operation on the original features to create new graph features, including new
node attributes and new graph topologies, and we combine them as new pairwise
inputs for specific GCNs. Then, we propose an attentional integrating model to
weighted sum the hidden node embeddings encoded by these GCNs into the final
node embeddings. We also conduct a disparity constraint on these hidden node
embeddings when training to ensure that non-redundant information is captured
from different features. Experimental results on five real-world datasets show
that our method improves the classification accuracy with a clear margin (+2.5%
- +84.2%) than the original GCN model.Comment: 16 pages, 6 figures,APWeb-WAIM 2021: The 5th APWeb-WAIM International
Joint Conference on Web and Big Dat