# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 3 Datasets and Notation  
### Datasets  
To use a GCN, we need to collect users' relationship graphs and features to build sample datasets. In addition, with the introduction of few-shot learning, we score a small number of samples in each category under the guidance of private security experts; these scores measure and grade the privacy status of users based on their characteristics. Then, we label the samples with the privacy scores. In this section, we will give a detailed description of the datasets and the extracted features we collect, which will provide the basis for subsequent method design and validation. Due to the lag in the research of privacy measurement in social networks and datasets involving user privacy, there are few public datasets. The existing datasets in privacy metrics research are mainly referenced from similar fields, such as data mining, sentiment analysis, recommendation systems, link inference, community discovery, and attribute inference. However, these datasets are collected for their specific areas and do not contain all the content we need. Even for social network datasets, their content is not comprehensive, either containing only user information and graph information or containing published text information. For example, Blogcatalog is a dataset of social networks whose graph is composed of bloggers and their social relations (such as friends); labels indicate the interests of the blogger. Reddit is composed of posts from the Reddit forum. Epicions is a multigraph dataset collected from an online product review website that contains the attitudes (trust/distrust) of reviewers towards other reviewers and the rating of products by reviewers. In our study, we combine a user's profile information, the relationship information in the user's network environment, the user's behaviour characteristics, and other extracted features. No public datasets contain all of this information. Therefore, we build our dataset according to our needs. Our data were collected on Sina Weibo, which is China's largest online social network, with 486 million monthly active users and 211 million daily active users. A user's friend relationships, profile, and publishing content are all public. We investigated nearly 500 students, teachers, and employees at our school. Approximately one-third of these individuals are users of Sina Weibo. In a further follow-up survey, we selected 16 users with certain differences as the seed of the crawling dataset according to the following criteria: (1) These users are active users of Sina Weibo. (2) These users have as many friend relationships as possible with other seed users. The reason for our requirements is to ensure that the final dataset conforms to the small-world and homogeneity principles and enables us to extract enough behaviour characteristics that relate users. If users are randomly selected from social networks, the resulting structure graphs may be disjoint. We use the crawled Sina Weibo user data to build two datasets and extract the features we need. The first dataset contains these 16 users and all their friends, including Follows and Followers. The second dataset contains the friends of these 16 users and the friends of their friends. Specific information and selected feature information are shown in Tables 1 and 2. The node structure of Dataset 1 is shown in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Nodes** & **Edges** \\ \hline Dataset 1 & 3244 & 6452 \\ Dataset 2 & 704,903 & 1,527,106 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets. It should be noted that when we crawled the dataset, we processed users with large numbers of followers differently because most of these users are public figures and official institutions, and studying them would have little relevance to the privacy disclosure of ordinary users. Without processing, the number of followers in only one public figure account will reach hundreds of millions.