Dr. ChenFang Zhang Profile

Dr. ChenFang Zhang

at Zhengzhou Univ of Light Industry

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Proceedings Article | 25 May 2022 Open Access

User identity alignment across heterogeneous networks based on meta-path attention

Yong Gan, Chenfang Zhang, Ruisen Yang

Proceedings Volume 12260, 122600Z (2022) https://doi.org/10.1117/12.2637544

KEYWORDS: Social networks, Feature extraction, Network security, Internet

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In order to establish a unified user model in multiple networks, a method of user identity alignment in social networks has been proposed. Mainly focusing on the user identity alignment with homogeneous network with only one type of node and edge, the former studies has been separated into three types: (1) studies based on network topology only, (2) studies based on user behavior only, (3) studies based on both user-generated content and network topology. But the defect of the former studies is obvious that there is no real social platform with only one type of node and edge in the network. This type of network is called a heterogeneous network. This paper proposes a model that can perform user identity alignment on heterogeneous networks, named user alignment across heterogeneous networks based on meta-path attention (MGUIL). MGUIL fuses meta-path features by introducing a graph attention mechanism in two heterogeneous networks and obtains local and global information through a two-layer GAT network, finally aligning the information in both networks with a unified framework. This method not only solves the alignment problem on heterogeneous network but also considers the global information propagation as a unified framework. We compare it with the existing method in real networks and confirm that MGUIL can improve user identity alignment accuracy.

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Proceedings Article | 25 May 2022 Open Access

User identity alignment across heterogeneous networks based on meta-path attention

Yong Gan, Chenfang Zhang, Ruisen Yang

Proceedings Volume 12260, 122600Z (2022) https://doi.org/10.1117/12.2637544

KEYWORDS: Social networks, Feature extraction, Network security, Internet

Read Abstract +

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