  Community detection, aiming to group the graph nodes into clusters with dense
inner-connection, is a fundamental graph mining task. Recently, it has been
studied on the heterogeneous graph, which contains multiple types of nodes and
edges, posing great challenges for modeling the high-order relationship between
nodes. With the surge of graph embedding mechanism, it has also been adopted to
community detection. A remarkable group of works use the meta-path to capture
the high-order relationship between nodes and embed them into nodes' embedding
to facilitate community detection. However, defining meaningful meta-paths
requires much domain knowledge, which largely limits their applications,
especially on schema-rich heterogeneous graphs like knowledge graphs. To
alleviate this issue, in this paper, we propose to exploit the context path to
capture the high-order relationship between nodes, and build a Context
Path-based Graph Neural Network (CP-GNN) model. It recursively embeds the
high-order relationship between nodes into the node embedding with attention
mechanisms to discriminate the importance of different relationships. By
maximizing the expectation of the co-occurrence of nodes connected by context
paths, the model can learn the nodes' embeddings that both well preserve the
high-order relationship between nodes and are helpful for community detection.
Extensive experimental results on four real-world datasets show that CP-GNN
outperforms the state-of-the-art community detection methods.
