Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2023
Abstract
On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal or even inapplicable on LHGs. To address the absence of type information, we propose a model named LHGNN, based on the novel idea of semantic embedding at node and path levels, to capture latent semantics on and between nodes. We further design a personalization function to modulate the heterogeneous contexts conditioned on their latent semantics w.r.t. the target node, to enable finer-grained aggregation. Finally, we conduct extensive experiments on four benchmark datasets, and demonstrate the superior performance of LHGNN.
Keywords
Latent heterogeneous graph, Link prediction, Graph neural networks
Discipline
Databases and Information Systems | OS and Networks
Publication
Proceedings of the World Wide Web Conference, WWW 2023, Austin TX, USA, April 30 - May 4
First Page
263
Last Page
273
ISBN
978145039416-1
Identifier
10.1145/3543507.3583284
Publisher
ACM
City or Country
New York, USA
Citation
NGUYEN, Trung Kien; LIU, Zemin; and FANG, Yuan.
Link prediction on latent heterogeneous graphs. (2023). Proceedings of the World Wide Web Conference, WWW 2023, Austin TX, USA, April 30 - May 4. 263-273.
Available at: https://ink.library.smu.edu.sg/sis_research/8190
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.