Large Graph Construction for Scalable Semi-Supervised Learning
Impact in
Classified as
- Authors
- Wei LiuJunfeng HeShih‐Fu Chang
In The Last Decade
doi.org/w71231820 →Countries where authors are citing Large Graph Construction for Scalable Semi-Supervised Learning
This map shows the geographic impact of Large Graph Construction for Scalable Semi-Supervised Learning. 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 Large Graph Construction for Scalable Semi-Supervised Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Large Graph Construction for Scalable Semi-Supervised Learning more than expected).
Fields of papers citing Large Graph Construction for Scalable Semi-Supervised Learning
This network shows the impact of Large Graph Construction for Scalable Semi-Supervised Learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Large Graph Construction for Scalable Semi-Supervised Learning.
About Large Graph Construction for Scalable Semi-Supervised Learning
This paper, published in 2010, received 350 indexed citations . Written by Wei Liu, Junfeng He and Shih‐Fu Chang covering the research area of Computational Mechanics, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (270 citations), Artificial Intelligence (187 citations), Media Technology (75 citations), Computational Mechanics (29 citations) and Urban Studies (28 citations).
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/w71231820.