Heterogeneous Graph Attention Network

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Presentation transcript:

Heterogeneous Graph Attention Network Source : WWW 2019 Advisor : JIA-LING KOH Speaker : YI-CHENG HUNG Date:2019/05/06

Outline Introduction Method Experiment Conclusion

Outline Introduction Method Experiment Conclusion

Introduction

Outline Introduction Method Experiment Conclusion

PRELIMINARY Definition 3.1. Heterogeneous Graph

PRELIMINARY Definition 3.2. Meta-path Movie-Actor-Movie (MAM) and Movie-Director-Movie (MDM).

PRELIMINARY Definition 3.3. Meta-path based Neighbors.

The overall framework of the proposed HAN.

Node-level Attention

Semantic-level Attention

Algorithm

Outline Introduction Method Experiment Conclusion

Dataset

Qantitative results (%) on the node classification task.

Qantitative results (%) on the node classification task.

Qantitative results (%) on the node classification task.

Meta-path based neighbors of node P831 and corresponding attention values

Analysis of semantic-level attention.

Qantitative results (%) on the node clustering task.

Visualization embedding on DBLP

Parameter sensitivity of HAN

CONCLUSION The proposed HAN can capture complex structures and rich semantics behind heterogeneous graph. The proposed model leverages nodelevel attention and semantic- level attention to learn the importance of nodes and meta-paths, respectively.