|
|
|
| Gender Recognition of Social Network Users Based on Heterogeneous Motif Features |
| XIANG Yuping, XU Xiaoke
|
| College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China |
|
|
|
|
Abstract User gender is one of the core aspects of user profiling, and existing methods for accurately identifying user gender mainly rely on user public attributes, with less consideration given to network structure information. This study integrates gender information based on motif theory, subdivides homogeneous motifs into heterogeneous motifs, and proposes a gender recognition method based on heterogeneous motif features, extracting more detailed local information to distinguish users of different genders. Compared to the current popular network embedding methods, the method proposed in this article has improved the Accuracy index by 2.8% to 14.2%, and the AUC index by 2.7% to 15.8%, with stable performance on different proportion training sets. The heteromorphic method can be applied to the identity detection of social users, which helps to conduct in-depth research on the structural characteristics of social networks.
|
|
Received: 06 July 2023
Published: 03 June 2025
|
|
|
|
|
|
|
|
|
