# ALPINE: Active Link Prediction Using Network Embedding
## 5 Experiments and Discussion
### Qualitative Evaluation of ALPINE  
In Section 1, we used the [PERSON] network to illustrate the idea of ALPINE with three of our query strategies, which focused on predicting the unknown links for a target node--\"[PERSON]\"--who has very limited observed information to the rest of the network. Now, we complete this qualitative evaluation with the same setting for other strategies: **page-rank.**, **d-opt.**, and **v-opt.**; **min-dis.** was omitted as it approximates **max-prob.  
Figure 2: Comparison of CNE_K and CNE.  
Table 3 shows the top five suggestions, and the relevant characters are highlighted in Figure 3 with their names. Since CNE achieved different local optima, here, we used a different two-dimensional visualization to better display the names. All the suggestions were reasonable and could be explained from different perspectives, proving that ALPINE with those query strategies made sense qualitatively. Similar to **max-deg.** and **max-prob.**, **page-rank.** and **d-opt.** had the same top three suggestions: [PERSON], [PERSON], and [PERSON], which are essential allies of [PERSON]. Knowing whether [PERSON] is linked to them will give a clear big picture of his social relations. The results can further be analyzed according to the strategy definitions.  
Strategy **page-rank.**, as **max-deg.**, aims to find out [PERSON]'s relationships with the _influucers_--nodes that are observed to have many links. With this type of strategy, we learned which _influucers_ [PERSON] is close to, as well as his potential allies connecting to them; and conversely for his unlinked _influucers_.  
The **d-opt.** strategy selects queries based on the parameter variance reduction. It implies that by knowing whether [PERSON] is linked to the suggested nodes, the node embeddings will have a smaller variance, such that the entire embedding space is more stable, and thus, the link predictions are more reliable. For example, [PERSON], who ranks the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **Page-Rank.** & **d-opt.** & **v-opt.** \\ \hline
1 & [PERSON] & [PERSON] & [PERSON] \\
2 & [PERSON] & [PERSON] & [PERSON] \\
3 & [PERSON] & [PERSON] & [PERSON] \\
4 & [PERSON] & [PERSON] & [PERSON] \\
5 & [PERSON] & [PERSON] & [PERSON] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top 5 query selections for the other three strategies of ALPINE.  
Figure 3: [PERSON] network with suggestions from Table 3 highlighted.  
fourth here (also the fourth with **max-ent.**), was not an obvious ally of [PERSON], but he helps secretly and is essential in shaping the network structure. The suggestions were considered uncertain and contributed to the reduction of the parameter variance.  
The **v-opt.** strategy quantifies the informativeness of the unobserved link statuses by the amount of estimated prediction variance reduction they cause. It suggests that [PERSON]'s relationships to the [PERSON] family are informative for minimizing the prediction variance for him. It makes sense as this family is well connected with [PERSON], who is [PERSON]'s known ally, and also connects well with other nodes. As for [PERSON], it was observed to be connected only to [PERSON] and unlinked to all other nodes except [PERSON]. Knowing whether [PERSON] and [PERSON] are linked greatly reduced the variance on the prediction for the unobserved, because there was no other information for it.  
We concluded that, intuitively, the query strategies resulted in expected behavior, although we caution against overinterpretation of this subjective qualitative evaluation. The next, quantitative, evaluation, provided an objective assessment of the merits of the query strategies, relative to passive learning, to each other, and to the single pre-existing method of which we we are aware.