# Learning Effective Feature Representation against User Privacy Protection on Social Networks
## 1 Introduction  
Network representation learning (NRL) is an essential technique in various social network analysis (SNA) and prediction tasks. The basic idea is to learn low dimensional feature representation vectors of nodes, so that the graph neighborhood of each user node can be encoded in the latent space. The performance of typical SNA tasks, such as link prediction, node classification, and community detection, can get improved due to network embeddings [1]. Several typical NRL models have been proposed, such as DeepWalk [2], LINE [3], and node2 vec [4]. The common concept is to generate contexts of nodes and apply the skip-gram model [5] to learn and produce the embedding vectors. In addition to preserving network topology, some studies are devoted to incorporate node attributes when learning their embeddings. Some representative models include VGAE [6], LANE [7], and DANE [8]. They enforce nodes not only possessing similar attributes, but sharing common graph neighborhood to be as close as possible in the embedding space. In online social networks, users are allowed to not only connect with their friends by creating new links with others, but also provide personal information, such as gender, age, interests, and employment, in their profiles. Friendships are used to create the social network while profiles are utilized as user attributes, which are associated on nodes in the network. Both social connections and user profiles are accumulated during the user-user and user-item interactions on social media [9]. Due to the effects of homophily and social circles [10], which state that users with similar attributes and common friends tend to group together, social connections and user attributes serve as essential roles when utilizing machine learning techniques to enable high-quality social services. By applying NRL methods to generate user representations, we are allowed to have better user profiling for bringing accurate applications, including computational advertising [11], identifying rumor spreaders [12], and recommender systems [13]. Although node embeddings that are derived by existing NRL models,such as those mentioned above, have been validated to be effective, these models presume that information on the social network is complete. However, real-world social networks are usually sparse in graph topology and user attributes due to privacy concerns. To be specific, existing NRL models need two requirements. The first is on complete network structures, meaning that users in the network have more or fewer connections. The second is on rich user profiles, referring to that most users share several personal attributes in their profiles. In other words, to the best of our knowledge, none of thw existing NRL models can afford user privacy protection. User privacy protection means that users hide their personal and social information. Insufficient user information leads to incomplete specification of friendships, i.e., networks containing a number of connected components, and rare personal profiles, i.e., most of the attributes are not provided. User privacy protection is not considered into datasets used for NRL. It is unrealistic to presume that all social connections and user profiles are complete and available. As users on social media tend to be aware of privacy protection and have the right to hide personal information, incomplete graph and attribute data becomes ubiquitous. An effective NRL model is required to generate useful node embeddings that afford such data incompleteness due to user privacy protection. Privacy-aware social networking applications (e.g., friend recommendation and user profiling), powered by privacy-protected feature representation learning, will not only build the trustfulness with users, but also boost their loyalty. Nevertheless, conventional NRL models, e.g., node2 vec [4] and DeepWalk [2], can still be executed to generate node embeddings from incomplete network data.