Abstract
Analysing bibliographic networks is important for understanding the process of scientific publications. A bibliographic network can be studied using the framework of Heterogeneous Information Networks (HINs). In this paper, we comparatively evaluate two different algorithms for link prediction in HINs on an instance of a bibliographic network. These two algorithms represent two distinct categories: algorithms that use path-related features of the graph and algorithms that use node embeddings. The results suggest that the path-based algorithms achieve significantly better performance on bibliographic networks.
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Acknowledgments
This work was partially funded by the EU H2020 project SmartDataLake (825041).
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Chronis, P., Skoutas, D., Athanasiou, S., Skiadopoulos, S. (2020). Link Prediction in Bibliographic Networks. In: Bellatreche, L., et al. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL ADBIS 2020 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-030-55814-7_28
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DOI: https://doi.org/10.1007/978-3-030-55814-7_28
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