Ralitsa Angelova
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 5%
- Information Systems top 10%
- Molecular Biology
- Computer Networks and Communications
- Co-authors
- Gerhard WeikumSrikanta BedathurGjergji KasneciPaweł PrałatEvangelos MiliosSusan DumaisEfthimis N. EfthimiadisDavid Hawking
- Topics
- Complex Network Analysis Techniques (5 papers)Advanced Graph Neural Networks (3 papers)Peer-to-Peer Network Technologies (2 papers)
- Journals
- World Wide WebInternational Journal of Data Warehousing and MiningMax Planck Institute for Plasma Physics
- Partner nations
- GermanyCanadaSwitzerland
In The Last Decade
Ralitsa Angelova
7 papers receiving 250 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 176
- Statistical and Nonlinear Physics 161
- Information Systems 73
- Molecular Biology 38
- Computer Networks and Communications 36
Countries citing papers authored by Ralitsa Angelova
This map shows the geographic impact of Ralitsa Angelova's research. 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 Ralitsa Angelova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ralitsa Angelova more than expected).
Fields of papers citing papers by Ralitsa Angelova
This network shows the impact of papers produced by Ralitsa Angelova. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ralitsa Angelova. The network helps show where Ralitsa Angelova may publish in the future.
Co-authorship network of co-authors of Ralitsa Angelova
This figure shows the co-authorship network connecting the top 25 collaborators of Ralitsa Angelova. A scholar is included among the top collaborators of Ralitsa Angelova based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ralitsa Angelova. Ralitsa Angelova is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 7 | |
| 3 | 1 | |
| 4 | 131 | |
| 5 | Characterizing a social bookmarking and tagging network | 7 |
| 6 | 95 | |
| 7 | Graph-based Text Classification: Learn from Your Neighbors | 5 |
| 8 | 0 |
About Ralitsa Angelova
Ralitsa Angelova is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Literature and Literary Theory, having authored 8 papers that have together received 262 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (5 papers), Advanced Graph Neural Networks (3 papers) and Peer-to-Peer Network Technologies (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (161 citations), Artificial Intelligence (176 citations) and Computational Mathematics (2 citations). Ralitsa Angelova has collaborated with scholars based in Germany, Canada and Switzerland. Frequent co-authors include Gerhard Weikum, Srikanta Bedathur, Gjergji Kasneci, Paweł Prałat, Evangelos Milios, Susan Dumais, Efthimis N. Efthimiadis, David Hawking, Fabian M. Suchanek and Stefan Siersdorfer. Their work appears in journals such as World Wide Web, International Journal of Data Warehousing and Mining and Max Planck Institute for Plasma Physics.
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.