GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
文献类型：会议论文
| 作者 | Wenyu Du; Shuai Yu; Min Yang; Qiang Qu; Jia Zhu |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Lyon, France |
| 英文摘要 | In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14093]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Wenyu Du,Shuai Yu,Min Yang,et al. GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding[C]. 见:. Lyon, France. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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