# Learning Effective Feature Representation against User Privacy Protection on Social Networks
## 1 Introduction
However, due to lacking of rich contexts regarding edge connections and node attributes, the quality of derived node embeddings could yield unsatisfying performance in downstream tasks, such as link prediction and node classification. In other words, these NRL models require graphs with better connectivity and rich user profiles to generate effective feature representations. It is apparent that more social connections and attributes imply that users need to provide more information. Most of them could be unavailable when users concern more about privacy protection. Few NRL models have connected user privacy with node embedding learning, such as DPNE [14] and PPGD [15]. However, their goals are to make the learned node embeddings possess the property of _differential privacy_[16], given complete social network data. That said, they are nothing to do with tackling incomplete and sparse network topology and user attributes in learning node embeddings. Another direction is to incorporate the concept of _fairness_ in NRL models [17; 18]. Sparse network data that result from user privacy protection are still not their concerns. In this work, we aim at learning effective node feature representations that can afford user privacy protection, which leads to incomplete and sparse social network data. Specifically, the essential setting of our work is that users in the network may not want to provide all of their connections or fill in all of the attribute fields. The learning of each node's feature representation only relies on a limited number of edge connections to neighbors and few publicly available attributes because most of them are hidden due to the privacy concern of users. We want the learned embeddings can still capture the semantics of nodes in both structural and attributed aspects. Technically speaking, we need to mitigate the damage of the sparse graph structure and incomplete node attributes in the process of embedding learning. Assume that the effectiveness of node representations is evaluated by the performance of downstream tasks, including node classification and link prediction. We expect that the effectiveness of node representations generated by privacy-protected incomplete social network data can still approach as closely as possible to that produced by using complete graph structure and attributes to learn node embeddings. While the conventional node embedding learning methods, such as node2 vec [4], DeepWalk [2], and LINE [3], do not take the data incompleteness and sparsity into consideration, the effectiveness of their generated node embeddings could be low. Our goal can also be seen as improving the effectiveness of their learned feature representations. More effectiveness improvement indicates that the derived node embeddings can better afford user privacy protection. We will present some techniques for dealing with such privacy-incurred data incompleteness so that the learned node embeddings are still effective in downstream tasks. The main idea of our method is two-fold. The first is the proposed Structure-Attribute Enhanced Matrix (SAEM). SAEM attempts to take full advantage of bare social connections and user attributes by making them mutually reinforced. User attributes are used to create ghost links between nodes so that the data sparsity can be alleviated. The second is the proposed community-cluster node2 vec (c2n2v) model. Our c2n2v model is able to further improve the quality of NRL through incorporating topology-based user communities and attribute-based user clusters. Experiments conducted on three network datasets, along with three settings that simulate user privacy protection (i.e., random, partial, and invisible). By performing tasks of community detection, link prediction, and node classification, the results demonstrate that our model can generate better node embeddings, as comparing to some baselines and the typical node2 vec method. This work is organized, as below. We first present the problem statement in Section 3, following by elaborating the proposed method in Section 4.