Slava Novgorodov
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Science Applications top 2%
- Management Science and Operations Research top 5%
- Computer Networks and Communications
- Co-authors
- Tova MiloWang-Chiew TanIdo GuyHaim KaplanKira RadinskyOhad GreenshpanNeoklis PolyzotisSusan B. Davidson
- Topics
- Data Stream Mining Techniques (9 papers)Mobile Crowdsensing and Crowdsourcing (9 papers)Web Data Mining and Analysis (8 papers)
- Cited by
- Computer Science ApplicationsManagement Science and Operations ResearchArtificial Intelligence
- Partner nations
- IsraelUnited StatesIreland
In The Last Decade
Slava Novgorodov
37 papers receiving 359 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 237
- Information Systems 136
- Computer Science Applications 122
- Management Science and Operations Research 98
- Computer Networks and Communications 59
Countries citing papers authored by Slava Novgorodov
This map shows the geographic impact of Slava Novgorodov'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 Slava Novgorodov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Slava Novgorodov more than expected).
Fields of papers citing papers by Slava Novgorodov
This network shows the impact of papers produced by Slava Novgorodov. 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 Slava Novgorodov. The network helps show where Slava Novgorodov may publish in the future.
Co-authorship network of co-authors of Slava Novgorodov
This figure shows the co-authorship network connecting the top 25 collaborators of Slava Novgorodov. A scholar is included among the top collaborators of Slava Novgorodov 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 Slava Novgorodov. Slava Novgorodov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 12 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 6 | |
| 7 | 2 | |
| 8 | 14 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 9 | |
| 12 | 9 | |
| 13 | 13 | |
| 14 | Managing General and Individual Knowledge in Crowd Mining Applications | 8 |
| 15 | 35 | |
| 16 | 21 | |
| 17 | 32 | |
| 18 | 17 | |
| 19 | Answering Planning Queries with the Crowd (Technical report) | 2 |
| 20 | 50 |
About Slava Novgorodov
Slava Novgorodov is a scholar working on Computer Science Applications, Management Science and Operations Research and Artificial Intelligence, having authored 39 papers that have together received 375 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (9 papers), Mobile Crowdsensing and Crowdsourcing (9 papers) and Web Data Mining and Analysis (8 papers). The work is most often cited by research in Computer Science Applications (122 citations), Management Science and Operations Research (98 citations) and Artificial Intelligence (237 citations). Slava Novgorodov has collaborated with scholars based in Israel, United States and Ireland. Frequent co-authors include Tova Milo, Wang-Chiew Tan, Ido Guy, Haim Kaplan, Kira Radinsky, Ohad Greenshpan, Neoklis Polyzotis, Susan B. Davidson, Yael Amsterdamer and T. Milo. Their work appears in journals such as Expert Systems with Applications, Proceedings of the VLDB Endowment and Information Retrieval.
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.