Directed Acyclic Graph Learning on Attributed Heterogeneous Network

doi:10.1109/TKDE.2023.3266453

Directed Acyclic Graph Learning on Attributed Heterogeneous Network

2023-10-01

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DOI：10.1109/TKDE.2023.3266453

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Learning the directed acyclic graph (DAG) among causal variables is a fundamental pre-task in causal discovery. Available DAG learning solutions canonically focus on homogeneous nodes with multiple variables and assume i.i.d. samples, how to learn DAG on typical attributed heterogeneous network (AHN) composed with different types of inter-dependent nodes and diverse attributes is a practical but more difficult task. In this paper, we propose HetDAG to identify DAG among nodes from heterogeneous network. HetDAG first embeds different types of node attributes and aggregates these embeddings as the node's raw representation. Then it uses contrastive learning with prior network structure to explore latent relationships between nodes and update the representation. Next, HetDAG introduces an attention-based DAG learning module that takes node representations as input to search DAG and orient edges between nodes. To the best of our knowledge, HetDAG is the first study to learn DAG on heterogeneous networks. Extensive experiments on both semi-synthetic and real data show that HetDAG can learn DAG in an efficacy way and outperforms the state-of-the-art approaches. The results on real biological networks confirm that HetDAG can find out the causal relations between lncRNAs and miRNAs.

Keywords:

Causality

contrastive learning

directed acyclic graph learning

gradient-based search

heterogeneous network embedding

Directed Acyclic Graph Learning on Attributed Heterogeneous Network

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