Heterogeneous Graph Attention Network

@article{Wang2019HeterogeneousGA,
  title={Heterogeneous Graph Attention Network},
  author={Xiao Wang and Houye Ji and Chuan Shi and Bai Wang and Peng Cui and Pinggang Yu and Yanfang Ye},
  journal={The World Wide Web Conference},
  year={2019}
}
Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. [] Key Method With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world…

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References

SHOWING 1-10 OF 40 REFERENCES

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

This work proposesMEta-GrAph-based network embedding models, called MEGA and MEGA++, respectively, which uses normalized relevance or similarity measures that are derived from a meta-graph and its embedded meta-paths between nodes simultaneously, and then leverages tensor decomposition method to perform node embedding.

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Two scalable representation learning models, namely metapath2vec and metapATH2vec++, are developed that are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, but also discern the structural and semantic correlations between diverse network objects.

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.

HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning

Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8%, in link prediction.

Gated Graph Sequence Neural Networks

This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

node2vec: Scalable Feature Learning for Networks

In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods.

Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

This paper re-examine similarity search in HINs and proposes a novel embedding-based framework, ESim, that accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space to explore network structure-embedded similarity.

Structural Deep Network Embedding

This paper proposes a Structural Deep Network Embedding method, namely SDNE, which first proposes a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non- linear network structure and exploits the first-order and second-order proximity jointly to preserve the network structure.

Modeling Relational Data with Graph Convolutional Networks

It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.

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.