research-article

MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks

Authors:

Lianhua ChiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 4

Article No.: 55, Pages 1 - 24

https://doi.org/10.1145/3564531

Published: 24 February 2023 Publication History

Abstract

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a clear and generic definition of implicit relationships. Then, we formalize the problem and propose an efficient solution, namely MIRROR, a graph convolutional network (GCN) model to infer implicit ties under explicit connections. MIRROR captures rich information in learning node-level representations by incorporating attributes from heterogeneous neighbors. Furthermore, MIRROR is tolerant of missing node attribute information because it is able to utilize network structure. We empirically evaluate MIRROR on four different genres of networks, achieving state-of-the-art performance for target relations mining. The underlying information revealed by MIRROR contributes to enriching existing knowledge and leading to novel domain insights.

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Cited By

Xu BWang JZhao ZLin HXia F(2024)HIM: Discovering Implicit Relationships in Heterogeneous Social NetworksICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447273(5875-5879)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447273
Liang YLiu LHuangfu LSamtani SYu ZZeng D(2023)Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363240618:3(1-24)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3632406
Tu HYu SSaikrishna VXia FVerspoor K(2023)Deep Outdated Fact Detection in Knowledge Graphs2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00184(1443-1452)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00184

Index Terms

MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks
1. Information systems
  1. World Wide Web
    1. Web mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 4

May 2023

364 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3583065

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 24 February 2023

Online AM: 22 September 2022

Accepted: 10 September 2022

Revised: 19 January 2022

Received: 21 February 2021

Published in TKDD Volume 17, Issue 4

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Cited By

Xu BWang JZhao ZLin HXia F(2024)HIM: Discovering Implicit Relationships in Heterogeneous Social NetworksICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447273(5875-5879)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447273
Liang YLiu LHuangfu LSamtani SYu ZZeng D(2023)Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363240618:3(1-24)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3632406
Tu HYu SSaikrishna VXia FVerspoor K(2023)Deep Outdated Fact Detection in Knowledge Graphs2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00184(1443-1452)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00184

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