# ALPINE: Active Link Prediction Using Network Embedding
## 5 Experiments and Discussion
### Discussion  
Our experiments showed that ALPINE in its general form can be adapted for various problem settings, and active learning performed consistently and substantially better than passive learning regardless of which of the investigated query strategies was applied. Now, we discuss how the strategies can be optimally applied based on our observations, with advice and insights that may help a practitioner select the best query strategy given the properties of the data and available computational resources.  
Figure 7: ALPINE on PPI_cc with \(s=74170\). Row 1: Case 1; Row 2: Case 2; Row 3: Case 3.  
Among the seven active learning query strategies we developed, **page-rank.** and **max-deg.** did not depend on the network embedding while the other five were embedding based. Thus, as with limited observed information, the network embedding might be of poor quality, in such cases, **page-rank.** and **max-deg.** were seen to outperform the others.  
The embedding-based strategies began to dominate when more information was observed and the embedding quality improved. The **max-ent.** and **v-opt.** had the top performance, but **d-opt.** had a more stable high performance across different values of \(r_{0}\). Based on those observations, we recommend a mixed strategy that starts from the degree-related and then switches to other embedding-based strategies.  
The complexity of the utility computation depended on the sizes of \(P\) and \(T\), as well as the network. Normally, the larger the pool, the more expensive the computations were, as we had to consider more candidate node pairs. All query strategies, including **rand.**, required a similar computing time when given the same size of \(P\). A notable exception is **v-opt.**, which was computationally more expensive. Yet, if we had a sufficiently accurate network embedding model, e.g., see the last columns in Figures 4-6, **v-opt.** was almost always the most accurate, especially for the first few iterations. Thus, when the cost of querying was high as compared to the cost of computations, **v-opt.** was preferable as soon as enough data were available such that the embedding was sufficiently accurate. If computational cost was a bottleneck though, **max-ent.** and **d-opt.** were computationally less expensive substitutes for **v-opt.**, with comparable accuracies.  
The experiments aimed to show how active learning, compared to passive learning, benefited the network embedding based link prediction, namely CNE. Therefore, following the line of research in active learning, we restricted our baselines to only the random and the state-of-the-art active learning strategies for link prediction [7; 9; 14; 25]. However, it would also be interesting to compare ALPINE with CNE against other types of link prediction methods to gain more insights. For example, a comparison of our work with a state-of-the-art link prediction approach (e.g., SEAL [45] according to [46; 47]) could be used to show whether the differentiation between the unknown and the unlinked status together with active learning would improve the link prediction accuracy in general. Note that this type of comparison can be biased as we had three types of link statuses, while other link prediction methods usually have only two. There are also other network embedding methods that can be used in combination with the ALPINE framework; thus  
Figure 8: ALPINE on Blog with \(s\) = 531,635. Row 1: Case 2; Row 2: Case 3.  
the comparison among CNE and other base models can be considered. That leaves many possible opportunities for research to be built on this work.