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| User Identification Across Social Media Based on Heterogeneous Graph Attention Network and Multi-Feature Fusion |
| Bi Datian, Zhang Xue, Kong Jingyuan, Chen Gongkun |
| School of Business and Management, Jilin University, Changchun130012 |
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Abstract Cross-social media user identification is crucial for guiding the collaborative governance of online public opinion and for comprehensively identifying and predicting user preferences. This study introduces a model for recognizing cross-social media users that integrates heterogeneous feature embedding and dynamic entity association to address the issues of weak data representability and neglect of the dynamic and associative nature of user information in current methods. The heterogeneous information networks of various social media platforms were created by incorporating basic user attributes, content generation, and social structure information. A new meta-path recognition strategy was designed to construct an adjacency matrix, allowing the heterogeneous graph attention network model to aggregate user node information and enhance the representability of node features. Additionally, three continuous time decay functions were introduced to weigh the entity similarity matrix across social media platforms, enhancing the dynamic relationship between entities. By integrating features from both single and cross-social networks, a multi-layer perceptron was utilized to achieve the recognition and prediction of cross-social media users. Experiments conducted on the real Weibo-Zhihu dataset showed that the overall performance of the model was superior to that of other benchmark models. The linear attenuation function was found to have the most significant impact, and the meta-path detection strategy proposed in this article played a pivotal role in improving detection effectiveness.
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Received: 16 October 2023
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