LookHops: light multi-order convolution and pooling for graph classification

Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive k-order(k>1) method requires more computation cost, limiting the further applications. In this paper, we investigate the strategy of selecting k via neighborhood information gain and propose light k-order convolution and pooling requiring fewer parameters while improving the performance. Comprehensive and fair experiments through six graph classification benchmarks show: 1) the performance improvement is consistent to the k-order information gain. 2) the proposed convolution requires fewer parameters while providing competitive results. 3) the proposed pooling outperforms SOTA algorithms in terms of efficiency and performance.