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	GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
	Wenyu Du; Shuai Yu; Min Yang; Qiang Qu; Jia Zhu
	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
内容类型	会议论文
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.

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