Semi-supervised learning using Gaussian fields and harmonic functions
- Journal
- UCL Discovery (University College London)
In The Last Decade
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This map shows the geographic impact of Semi-supervised learning using Gaussian fields and harmonic functions. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Semi-supervised learning using Gaussian fields and harmonic functions with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Semi-supervised learning using Gaussian fields and harmonic functions more than expected).
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This network shows the impact of Semi-supervised learning using Gaussian fields and harmonic functions. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Semi-supervised learning using Gaussian fields and harmonic functions.
About Semi-supervised learning using Gaussian fields and harmonic functions
This paper, published in 2003, received 2.3k indexed citations . Written by Xiaojin Zhu, Zoubin Ghahramani and John Lafferty covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (1.5k citations), Computer Vision and Pattern Recognition (1.1k citations) and Statistical and Nonlinear Physics (285 citations). Published in UCL Discovery (University College London).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w17873566.