mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding

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mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding
Title:
mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding
Other Titles:
IEEE Transactions on Knowledge and Data Engineering
Publication Date:
07 May 2020
Citation:
W. Zhang, Y. Fang, Z. Liu, M. Wu and X. Zhang, "mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2020.2992500.
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.
License type:
PublisherCopyrights
Funding Info:
This research / project is supported by the Ministry of Education, Singapore under its Academic Research Funding Tier 1 (18-C220-SMU-006).
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1041-4347
1558-2191
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