Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model

• URL: http://arxiv.org/abs/2109.02058v1
• Date: Sun, 5 Sep 2021 12:28:00 GMT
• Title: Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
• Authors: Linhao Luo, Yixiang Fang, Xin Cao, Xiaofeng Zhang, Wenjie Zhang
• Abstract summary: We build a Context Path-based Graph Neural Network (CP-GNN) model. It embeds the high-order relationship between nodes into the node embedding. It outperforms the state-of-the-art community detection methods.
• Score: 23.525079144108567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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