# ALPINE: Active Link Prediction Using Network Embedding
## 6 Conclusions  
Link prediction is an important task in network analysis, tackled increasingly using network embeddings. It is particularly important in partially observed networks, where finding out whether a node pair is linked is time consuming or costly, such that for a large number of node pairs, it is not known if they are linked. We proposed to make use of active learning in this setting and studied the problem of active learning for link prediction using network embedding in this paper.  
More specifically, we proposed the ALPINE framework, a method that actively learns to embed partially observed networks to achieve better link predictions, by querying the labels of the most informative unobserved link statuses. We developed several utility functions for ALPINE to quantify the utility of a node pair: some heuristically motivated and some derived as variance reduction methods based on D-optimality and V-optimality from optimal experimental design.  
We implemented ALPINE in combination with Conditional Network Embedding (CNE). To accomplish this, we first adapted CNE to work for partially observed networks. Through experimental investigation, we found that this modified version of CNE was not only more time efficient, but also more accurate for link prediction--an important side-result of the present paper.  
We then empirically evaluated the performance of the utility functions we developed for ALPINE, both qualitatively and quantitatively, providing insights into the merits of ALPINE and advice for practitioners on how to optimally apply this method to different problem settings.  
More broadly, the application of active learning to the link prediction problem in general, which is usually for partially observed networks, could help us to build more realistic and practical methods. Taking this work as a starting point, we see interesting future directions, including the investigation of a mixed strategy, batch mode active learning for ALPINE, and the application of ALPINE to the cold-start problem in recommender systems. Meanwhile, a thorough comparison of ALPINE with CNE against general link prediction methods, as well as the choice of the base network embedding model to be used with the ALPINE framework remain to be further investigated.  
Conceptualization, X.C., B.K. and T.D.B.; methodology, X.C., B.K. and T.D.B.; software, X.C. and B.K.; validation, X.C.; formal analysis, X.C., B.K. and T.D.B.; investigation, X.C. and B.K.; resources, X.C., B.K., J.L. and T.D.B.; data curation, X.C. and B.K.; writing--original draft preparation, X.C.; writing--review and editing, B.K., J.L. and T.D.B.; visualization, X.C. and J.L.; supervision, J.L. and T.D.B.; project administration, X.C. and T.D.B.; funding acquisition, J.L. and T.D.B.. All authors read and agreed to the published version of the manuscript.  
The research leading to these results received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 615517, from the Flemish Government under the \"Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen\" program, from the FWO (Project Nos. G091017N, G0F9816N, 3G042220), and from the European Union's Horizon 2020 research and innovation program and the FWO under the Marie Sklodowska-Curie Grant Agreement No. 665501.  
The research leading to these results received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) (ERC Grant Agreement no. 615517), and under the European Union's Horizon 2020 research and innovation programme (ERC Grant Agreement no. 963924), from the Flemish Government under the \"Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen\" programme, and from the FWO (project no. G091017N, G0F9816N, 3G042220). We thank [PERSON] for helping collect the MP network data.  
The authors declare no conflict of interest.