Graph Neural Networks (GNNs) have become a promising approach to machine
learning with graphs. Since existing GNN models are based on flat
message-passing mechanisms, two limitations need to be tackled. One is costly
in encoding global information on the graph topology. The other is failing to
model meso- and macro-level semantics hidden in the graph, such as the
knowledge of institutes and research areas in an academic collaboration
network. To deal with these two issues, we propose a novel Hierarchical
Message-Passing Graph Neural Networks framework. The main idea is to generate a
hierarchical structure that re-organises all nodes in a graph into multi-level
clusters, along with intra- and inter-level edge connections. The derived
hierarchy not only creates shortcuts connecting far-away nodes so that global
information can be efficiently accessed via message passing but also
incorporates meso- and macro-level semantics into the learning of node
embedding. We present the first model to implement this hierarchical
message-passing mechanism, termed Hierarchical Community-aware Graph Neural
Network (HC-GNN), based on hierarchical communities detected from the graph.
Experiments conducted on eight datasets under transductive, inductive, and
few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN
models in network analysis tasks, including node classification, link
prediction, and community detection
