.png){kind=icon}
.png){kind=icon}
Return
Multi-View Interactive Heterogeneous Graph Contrastive Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TNSE.2025.3595875.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Generating discriminative node representations for heterogeneous graphs based on contrastive learning with graph neural networks has become an important topic in data mining. Many existing contrastive learning methods for heterogeneous graphs rely on meta-path information to establish contrastive views and train graph encoders. However, these algorithms have not yet taken full advantage of the wealth of information contained in heterogeneous graphs, encompassing diverse node connections and semantic insights from various meta-paths. Moreover, they do not pay sufficient attention to the encoder adaptation to model properties and the selection of appropriate positive samples between views. To address these problems, we propose a novel Multi-view interactive heterogeneous Graph Contrastive learning (MiCo) model, which leverages positive samples derived from meta-path topology to capture intricate information within a heterogeneous graph effectively. Specifically, we first adopt a multi-view strategy to characterize the various levels of information from a heterogeneous graph, while also selecting suitable encoders tailored to specific views. Then, each unique positive instance is determined according to the topological structure of the target node on each meta-path. Finally, we employ temperature coefficients at different stages to distinguish between the challenging and straightforward negative samples within the negative sample set. Extensive experiments show that MiCo consistently outperforms state-of-the-art baselines on four real-world datasets: ACM, DBLP, AMiner, and IMDB.
Keywords:
Heterogeneous graph
contrastive learning
multi-view interaction
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K