A Bayesian Method for Link Prediction with Considering Path Information
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN: 1867-8211, Vol: 294 LNCIST, Page: 361-374
2019
- 3Citations
- 4Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Predicting links among nodes in the network is an interesting and practical problem. Many link prediction methods based on local or global topology alone have been proposed. There is a need to combine these two types of methods to further improve the prediction performance. In line with this direction, we study the link prediction problem based on the Bayesian method and propose a new link prediction method, i.e., path-based Bayesian (PB) method. In this prediction method, we give the definition of clustering coefficients of paths and use it to quantify the contribution of paths to link generation. Then, we propose a new link prediction method by combining the clustering coefficient of paths and Bayesian theory. Simulation results on real-world networks show that our prediction method has higher prediction accuracy than the mainstream methods.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85076147607&origin=inward; http://dx.doi.org/10.1007/978-3-030-32388-2_31; http://link.springer.com/10.1007/978-3-030-32388-2_31; https://dx.doi.org/10.1007/978-3-030-32388-2_31; https://link.springer.com/chapter/10.1007/978-3-030-32388-2_31
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know