A Multiple Salient Features-Based User Identification across Social Media

Qu, Yating; Wu, Honghai; Zhang, Kun; Deng, Kaikai

doi:10.3390/e24040495

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…MAH [22] Based on the traditional network topology, the semi-supervised learning model realizes user identification across two social networks through a small number of seed nodes, and efficiently uses a small amount of annotated data.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

Huang,

Xiang,

Leng

et al. 2024

Electronics

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Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

Huang,

Xiang,

Leng

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Users' writing patterns, personal emotion, and other semantic features can be mined through an analysis of usernames and text posts [11]. Integrated consideration of the username, user-generated content, geographic location, network topology, and other data can help mine users' semantic features, comprehensively characterize users, and reduce the negative impacts of local feature differences on user-alignment effects [12][13][14][15]. Notably, however, user feature mining methods discussed above do not consider the reliability of data, computing overhead, and missing data problems.…”

Section: Introductionmentioning

confidence: 99%

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

Huang

Zhao

Zhang

et al. 2023

Entropy

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User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user’s semantic features for these three factors. In addition, we improve the algorithm’s adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment.

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Generating-Based Attacks to Online Social Networks

Gao,

Bian,

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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A Multiple Salient Features-Based User Identification across Social Media

Cited by 5 publications

References 38 publications

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

Generating-Based Attacks to Online Social Networks

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