  We present a multi-filtering Graph Convolution Neural Network (GCN) framework
for network embedding task. It uses multiple local GCN filters to do feature
extraction in every propagation layer. We show this approach could capture
different important aspects of node features against the existing attribute
embedding based method. We also show that with multi-filtering GCN approach, we
can achieve significant improvement against baseline methods when training data
is limited. We also perform many empirical experiments and demonstrate the
benefit of using multiple filters against single filter as well as most current
existing network embedding methods for both the link prediction and node
classification tasks.
