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Heterogeneous Graph Attention Network

Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of the proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
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Structural Deep Clustering Network

A Structural Deep Clustering Network (SDCN) is proposed to integrate the structural information into deep clustering, with a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model.
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Heterogeneous Information Network Embedding for Recommendation

A novel heterogeneous network embedding based approach for HIN based recommendation, called HERec is proposed, which shows the capability of the HERec model for the cold-start problem, and reveals that the transformed embedding information from HINs can improve the recommendation performance.
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A Survey of Heterogeneous Information Network Analysis

This paper will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.
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HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects, which is crucial to many data mining tasks.
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Beyond Low-frequency Information in Graph Convolutional Networks

An experimental investigation assessing the roles of low-frequency and high-frequency signals is presented, and a novel Frequency Adaptation Graph Convolutional Networks (FAGCN) with a self-gating mechanism is proposed, which can adaptively integrate different signals in the process of message passing.
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Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model

A novel deep neural network with the co-attention mechanism for leveraging rich meta-path based context for top-N recommendation and performs well in the cold-start scenario and has potentially good interpretability for the recommendation results.
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AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

This paper proposes an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN), which extracts the specific and common embeddings from node features, topological structures, and their combinations simultaneously, and uses the attention mechanism to learn adaptive importance weights of the embeddeddings.
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Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation

A metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation as a Heterogeneous Information Network is proposed and Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.
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Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

This paper proposes a novel co-contrastive learning mechanism for HGNNs, named HeCo, which differs from traditional contrastive learning which only focuses on contrasting positive and negative samples, and employs cross-view contrastive mechanism.
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