  It is not until recently that graph neural networks (GNNs) are adopted to
perform graph representation learning, among which, those based on the
aggregation of features within the neighborhood of a node achieved great
success. However, despite such achievements, GNNs illustrate defects in
identifying some common structural patterns which, unfortunately, play
significant roles in various network phenomena. In this paper, we propose
GraLSP, a GNN framework which explicitly incorporates local structural patterns
into the neighborhood aggregation through random anonymous walks. Specifically,
we capture local graph structures via random anonymous walks, powerful and
flexible tools that represent structural patterns. The walks are then fed into
the feature aggregation, where we design various mechanisms to address the
impact of structural features, including adaptive receptive radius, attention
and amplification. In addition, we design objectives that capture similarities
between structures and are optimized jointly with node proximity objectives.
With the adequate leverage of structural patterns, our model is able to
outperform competitive counterparts in various prediction tasks in multiple
datasets.
