DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

Xiong, Zhihao; Xie, Xing; Wu, Xu; Yuan, Peng; Lu, Yueming

doi:10.1109/icbda57405.2023.10104627

2023 IEEE 8th International Conference on Big Data Analytics (ICBDA) 2023

DOI: 10.1109/icbda57405.2023.10104627

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DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

Zhihao Xiong

Xing Xie

Xu Wu

et al.

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2024

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Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Xie,

Zang,

et al. 2024

Electronics

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Random-walk-based graph representation methods have been widely applied in User Identity Linkage (UIL) tasks, which links overlapping users between two different social networks. It can help us to obtain more comprehensive portraits of criminals, which is helpful for improving cyberspace governance. Yet, random walk generates a large number of repeating sequences, causing unnecessary computation and storage overhead. This paper proposes a novel method called Edge-Removing Walk (ERW) that can replace random walk in random-walk-based models. It removes edges once they are walked in a walk round to capture the l−hop features without repetition, and it walks the whole graph for several rounds to capture the different kinds of paths starting from a specific node. Experiments proved that ERW can exponentially improve the efficiency for random-walk-based UIL models, even maintaining better performance. We finally generalize ERW into a general User Identity Linkage framework called ERW-UIL and verify its performance.

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Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Xie,

Zang,

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

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DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

Cited by 1 publication

References 21 publications

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

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