Inferring Anchor Links Based on Social Network Structure

Feng, Shuo; Shen, Derong; Nie, Tiezheng; Kou, Yue; He, Jingrui; Yu, Ge

doi:10.1109/access.2018.2814000

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…Network-topology-based approaches compare the friend network similarity of users in the source and target networks to achieve user alignment [18,[31][32][33][34][35][36][37][38]. At present, network representation learning methods are commonly used to mine network topology features [35].…”

Section: User Alignmentmentioning

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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Section: User Alignmentmentioning

confidence: 99%

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

Huang

Zhao

Zhang

et al. 2023

Entropy

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“…Network alignment problem has drawn considerable research interests in recent years. This problem has been widely applied in many application areas such as database schema matching (Melnik et al, 2002), data mining (Bayati et al, 2009), social network (Feng et al, 2018), computer vision (Yang et al, 2018a) to bioinformatics (Hashemifar & Xu, 2014;Singh et al, 2008). Recent approaches can be categorized into unsupervised and supervised alignment methods.…”

Section: Network Alignmentmentioning

confidence: 99%

Structural representation learning for network alignment with self-supervised anchor links

Nguyen

Pham

Nguyên

et al. 2021

Expert Systems with Applications

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“…Zhou et al [26] propose FRUI (Friend Relationship-Based User Identification) algorithm which iteratively align users by calculating a structure based matching degree for all candidate user matched pairs. Feng et al [33] propose three similarity metrics (CPS,CCS, and CPS + ) for users across networks. By mathematically proving the effectiveness of these similarities, a two-stage iterative algorithm CPCC is used for the alignment.…”

Section: Related Work a User Alignment Across Social Networkmentioning

confidence: 99%

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

Liu

Zhang²,

et al. 2019

IEEE Access

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User alignment across social networks can facilitate more information/knowledge transferring across networks and thereby benefit several applications, including social link prediction, cross-domain recommendation, and information diffusion. Several works try to learn a common subspace for networks by preserving the structural proximities, such that different contribution weights of neighbors are ignored as users were always connected by unweighted edges. In this paper, we propose an attention-based network embedding model that exploits the social structures for user alignment. In particular, two main components are contained in our model framework: a masked graph attention mechanism which tries to learn the alignment task driven attention weights by the supervision of pre-aligned user pairs, and an embedding algorithm tries to learn a common vector space by explicitly modeling the weighted contribution probabilities between follower-ships and followee-ships. With the learned weights and embeddings transferring between these two components, we construct a unified model for user embedding and alignment. Stochastic gradient descent and negative sampling are adopted for efficient learning and scalability. The extensive experiments on real-world social network data sets demonstrate the effectiveness and robustness of the proposed model compared with several state-of-the-art methods. INDEX TERMS Attention mechanism, network embedding, social network analysis, user alignment.

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Inferring Anchor Links Based on Social Network Structure

Cited by 11 publications

References 28 publications

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

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

Structural representation learning for network alignment with self-supervised anchor links

ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

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