# Learning Effective Feature Representation against User Privacy Protection on Social Networks
## 5 Experiments
### Evaluation Settings  
_Datasets._ We use three network datasets in the experiments, including Cora, Citeceer, and Twitter Cora and Citeseer are benchmark network datasets ([[https://linqs.soe.ucsc.edu/data](https://linqs.soe.ucsc.edu/data)]([https://linqs.soe.ucsc.edu/data](https://linqs.soe.ucsc.edu/data))) for NRL. The Twitter dataset consists of top-10 large ego-networks ([[https://snap.stanford.edu/data/ego-Twitter.html](https://snap.stanford.edu/data/ego-Twitter.html)]([https://snap.stanford.edu/data/ego-Twitter.html](https://snap.stanford.edu/data/ego-Twitter.html))). We present their statistics in Table 1, in which values of Twitter are averaged.  
_Simulation of User Privacy Protection._ This evaluation is created to simulate the network incompleteness and sparsity, resulting from user privacy protection. We think the data incompleteness usually comes from that users do not want to reveal their personal information, such as friendships and attributes, due to some privacy concerns. Therefore, we use the following three strategies to generate privacy-protected social networks, and examine whether our NRL model can better survive from the sparsity. The first is **Random**: simulating every user can hide some information. We randomly remove \(\beta\%\) of all edges and all users' attributes from the original social network. The second is **Pritial(0.5)**: simulating only some users hide some information. We randomly remove \(\beta\%\) of edges and attributes for every of 50% randomly selected users. The third is **Invisible**: simulating some users totally hide their information. We randomly remove all edges and attributes for every of \(\beta\%\) randomly selected users. We repeat the \(\beta\%\) randomness up to 100 times, and report the average results.  
_Evaluation Plans._ The evaluation consists of three main parts for community detection (CD), link prediction (LP), and node classification (NC). For each part, we present the results (y-axis) on three different simulations of privacy protection by changing \(\beta\%\), i.e., reserving \(1-\beta\%\) information (x-axis). Note that we do not report the results of NC for Twitter data, since it contains no node labels. In addition, we also exhibit the sensitivity of hyperparameter \(k\) and \(\alpha\) that determines SAEM using NC. We use t-SNE [28] to visualize the learned node embeddings.  
_Competing Methods._ (1) **adj**: using only the adjacency matrix for node features, (2) **att**: using only user attributes as node features, (3) **adj+att**: concatenating adjacency vectors and user attributes as node features, (4) **n2v**: the typical NRL model node2 vec [4], (5) **SAEM**: using the proposed enhanced matrix for node features, (6) **SAEM+n2v**: applying node2 vec to SAEM for embedding learning, and (7) **SAEM+c2n2v**: combining the proposed SAEM with our c2n2v to generate node embeddings. Note that for link prediction, we do not have the first three competing methods because Hadamard product is used for only embedding vectors.  
_Evaluation Metrics._ For community detection, we employ Normalized Mutual Information (NMI) [29]. For link prediction, we utilize Area Under Curve (AUC) scores. For node classification, we consider Macro-F1 (MAF) as the metric. Higher values indicate better performance in all metrics. We consider these evaluation metrics because they are benchmarks and typically and widely used for experiments in the literature of network representation learning [2; 3; 4; 8; 19; 20].