Paper Info
Title
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
Abstract
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information. However, current relevance computing based on meta-graph only considers the complex structural information, but ignores its embedded meta-paths information. To address this problem, we proposeMEta-GrAph-based network embedding models, called MEGA and MEGA++, respectively. The MEGA model 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. The MEGA++ further facilitates the use of coupled tensor-matrix decomposition method to obtain a joint embedding for nodes, which simultaneously considers the hidden relations of all meta information of a meta-graph. Extensive experiments on two real datasets demonstrate that MEGA and MEGA++ are more effective than state-of-the-art approaches.
Year
DOI
Venue
2018
10.1109/ICBK.2018.00025
2018 IEEE International Conference on Big Knowledge (ICBK)
Keywords
DocType
Volume
network embedding,heterogeneous information networks,tensor learning
Conference
abs/1809.04110
ISBN
Citations 
PageRank 
978-1-5386-9126-7
4
0.44
References 
Authors
0
7
Name
Order
Citations
PageRank
Lichao Sun19414.15
Lifang He236932.74
Zhipeng Huang3886.16
Bokai Cao422316.70
Congying Xia5226.49
Xiaokai Wei6858.44
Philip S. Yu7306703474.16