mg2vec: Learning relationship-preserving heterogeneous graph representations via metagraph embedding
Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2022
Abstract
Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general HIN embedding. In this paper, we leverage metagraphs to learn relationship-preserving HIN embedding in a self-supervised setting, to support various relationship mining tasks. In particular, we observe that most of the current approaches often under-utilize metagraphs, which are only applied in a pre-processing step and do not actively guide representation learning afterwards. Thus, we propose the novel framework of mg2vec, which learns the embeddings for metagraphs and nodes jointly. That is, metagraphs actively participates in the learning process by mapping themselves to the same embedding space as the nodes do. Moreover, metagraphs guide the learning through both first- and second-order constraints on node embeddings, to model not only latent relationships between a pair of nodes, but also individual preferences of each node. Finally, we conduct extensive experiments on three public datasets. Results show that mg2vec significantly outperforms a suite of state-of-the-art baselines in relationship mining tasks including relationship prediction, search and visualization.
Keywords
heterogeneous information networks, network embedding, relationship mining
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
34
Issue
3
First Page
1317
Last Page
1329
ISSN
1041-4347
Identifier
10.1109/TKDE.2020.2992500
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
ZHANG, Wentao; FANG, Yuan; LIU, Zemin; WU, Min; and ZHANG, Xinming.
mg2vec: Learning relationship-preserving heterogeneous graph representations via metagraph embedding. (2022). IEEE Transactions on Knowledge and Data Engineering. 34, (3), 1317-1329.
Available at: https://ink.library.smu.edu.sg/sis_research/5128
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TKDE.2020.2992500