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Inferring Users' Social Roles with a Multi-Level Graph Neural Network Model
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Chunrui Wang, Shen Zhan, Dechen Yin, Mingyong Lou, Fang |
| Copyright Year | 2021 |
| Description | Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method. |
| Starting Page | 1453 |
| e-ISSN | 10994300 |
| DOI | 10.3390/e23111453 |
| Journal | Entropy |
| Issue Number | 11 |
| Volume Number | 23 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-01 |
| Access Restriction | Open |
| Subject Keyword | Entropy Information and Library Science Network Representation Learning Graph Neural Networks Social Networks Social Status and Role Inference |
| Content Type | Text |
| Resource Type | Article |
