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Shen Y, Jiang X, Li Z, Wang Y, Xu C, Shen H, Cheng X. UniSKGRep: A unified representation learning framework of social network and knowledge graph. Neural Netw 2023; 158:142-153. [PMID: 36450187 DOI: 10.1016/j.neunet.2022.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/16/2022] [Accepted: 11/06/2022] [Indexed: 11/16/2022]
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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Affiliation(s)
- Yinghan Shen
- Data Intelligent System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.
| | - Xuhui Jiang
- Data Intelligent System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Zijian Li
- Data Intelligent System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yuanzhuo Wang
- Data Intelligent System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Zhongke Big Data Academy, Zhengzhou, Henan Province, China.
| | - Chengjin Xu
- International Digital Economy Academy, Shenzhen, Guangdong Province, China
| | - Huawei Shen
- Data Intelligent System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xueqi Cheng
- Data Intelligent System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Network data and Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Amara A, Hadj Taieb MA, Ben Aouicha M. Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users. ONLINE INFORMATION REVIEW 2022. [DOI: 10.1108/oir-01-2021-0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.Design/methodology/approachWe propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.FindingsUsing the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.Originality/valueThis paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.
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