M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive
  Learning
Yuanyuan Guo Yu Xia Rui Wang Rongcheng Duan Lu Li Jiangmeng Li
Inspired by the successful application of contrastive learning on graphs,
researchers attempt to impose graph contrastive learning approaches on
heterogeneous information networks. Orthogonal to homogeneous graphs, the types
of nodes and edges in heterogeneous graphs are diverse so that specialized
graph contrastive learning methods are required. Most existing methods for
heterogeneous graph contrastive learning are implemented by transforming
heterogeneous graphs into homogeneous graphs, which may lead to ramifications
that the valuable information carried by non-target nodes is undermined thereby
exacerbating the performance of contrastive learning models. Additionally,
current heterogeneous graph contrastive learning methods are mainly based on
initial meta-paths given by the dataset, yet according to our deep-going
exploration, we derive empirical conclusions: only initial meta-paths cannot
contain sufficiently discriminative information; and various types of
meta-paths can effectively promote the performance of heterogeneous graph
contrastive learning methods. To this end, we propose a new multi-scale
meta-path integrated heterogeneous graph contrastive learning (M2HGCL) model,
which discards the conventional heterogeneity-homogeneity transformation and
performs the graph contrastive learning in a joint manner. Specifically, we
expand the meta-paths and jointly aggregate the direct neighbor information,
the initial meta-path neighbor information and the expanded meta-path neighbor
information to sufficiently capture discriminative information. A specific
positive sampling strategy is further imposed to remedy the intrinsic
deficiency of contrastive learning, i.e., the hard negative sample sampling
issue. Through extensive experiments on three real-world datasets, we
demonstrate that M2HGCL outperforms the current state-of-the-art baseline
models.