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research-article

UniSKGRep: : A unified representation learning framework of social network and knowledge graph

Authors:

Xueqi ChengAuthors Info & Claims

Volume 158, Issue C

Pages 142 - 153

https://doi.org/10.1016/j.neunet.2022.11.010

Published: 01 January 2023 Publication History

Abstract

The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two embedding spaces, respectively. Then the Inter-graph model is learned to associate the two separate spaces via bridging the semantics of overlapping node pairs. In addition, the overlapping node enhancement module is designed to effectively align two spaces with the consideration of a relatively small number of overlapping nodes. The two spaces are gradually unified by continuously iterating the joint training procedure. Extensive experiments on two real-world SKG datasets have proved the effectiveness of UniSKGRep in yielding general and substantial performance improvement compared with the strong baselines in various downstream tasks.

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Cited By

Zheng ZZhou SXu HGu MXu YLi ALi YGu JBu J(2025)Heterophilous distribution propagation for Graph Neural NetworksNeural Networks10.1016/j.neunet.2024.107014184:COnline publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1016/j.neunet.2024.107014
Ma RPang GChen L(2024)Harnessing collective structure knowledge in data augmentation for graph neural networksNeural Networks10.1016/j.neunet.2024.106651180:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106651
Theodoropoulos CMulligan NStappenbeck TBettencourt-Silva J(2023)Representation Learning for Person or Entity-Centric Knowledge Graphs: An Application in HealthcareProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627545(225-233)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627545

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UniSKGRep: A unified representation learning framework of social network and knowledge graph

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Information & Contributors

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Published In

cover image Neural Networks

Neural Networks Volume 158, Issue C

Jan 2023

386 pages

ISSN:0893-6080

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Elsevier Ltd.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 January 2023

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Cited By

Zheng ZZhou SXu HGu MXu YLi ALi YGu JBu J(2025)Heterophilous distribution propagation for Graph Neural NetworksNeural Networks10.1016/j.neunet.2024.107014184:COnline publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1016/j.neunet.2024.107014
Ma RPang GChen L(2024)Harnessing collective structure knowledge in data augmentation for graph neural networksNeural Networks10.1016/j.neunet.2024.106651180:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106651
Theodoropoulos CMulligan NStappenbeck TBettencourt-Silva J(2023)Representation Learning for Person or Entity-Centric Knowledge Graphs: An Application in HealthcareProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627545(225-233)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627545

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