Abstract
This paper summarizes several surveys of Link Prediction methods. It starts with a background introduction and problem definition, then provides information about several Link Prediction methods found in many surveys. It has been written with the aim of providing an assistive summary of Link Prediction methods for researchers in this field. Link Prediction is important since it has many applications including recommending friends in social networking and recommending products for customers to buy in e-commerce. It works by predicting which links are more likely to form in the future based on the local (neighborhood) or the global structure of the graph. This paper is a surveys' summary. It is beneficial because surveys are important in providing a very good review, reference, and comprehensive coverage of the topic. By having a good survey, researchers and readers can save a great amount of time by only looking at fewer papers. Several methods found in survey papers have been summarized in this work including Common Neighbors, Preferential Attachment, Jaccard, Adamic/Adar, SimRank, PageRank, Probabilistic, Node-based, Topology, Path-based, Random Walk, Learning, Quasi, and others. It has been found out that the survey paper with the highest citation has 2827 citations, followed by the second highest which is 643. This paper is a summary of survey papers, and should be distinguished from traditional surveys, that summarize Link Prediction methods discussed in one or more non-survey papers. This paper is the first paper of its kind in this field.
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Rawashdeh, A. (2023). A Brief Summary of Selected Link Prediction Surveys. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_15
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