Haggai Roitman

1.4k citations
75 papers · 869 indexed · h-index 19

Haggai Roitman

75 papers receiving 813 citations

Peers

Haggai Roitman
Comparison fields: 5 of 74
  • Information Systems 402
  • Artificial Intelligence 478
  • Signal Processing 130
  • Management Science and Operations Research 114
  • Computer Science Applications 46
Replace Richard McCreadie with:
Richard McCreadie United Kingdom
Naama Zwerdling Israel
Conor Hayes Ireland
Dhruv Gupta Germany
Ernesto William De Luca Germany
Leandro Balby Marinho Brazil
Nadav Golbandi Israel
Josiane Mothe France
Pável Calado Portugal
Samuel Ieong United States
Haggai Roitman relative to Richard McCreadie United Kingdom Richard McCreadie's profile →
Citations per field
00.5×1.5×
Richard McCreadie · 1×
Citations per year

Countries citing papers authored by Haggai Roitman

Since Specialization
Citations

This map shows the geographic impact of Haggai Roitman'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 Haggai Roitman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haggai Roitman more than expected).

Fields of papers citing papers by Haggai Roitman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Haggai Roitman. 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 Haggai Roitman. The network helps show where Haggai Roitman may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Haggai Roitman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Haggai Roitman Line = papers co-authored together Haggai Roitman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20208
3 20207
4 202023
5 20202
6 20198
7 20184
8 20179
9 201721
10 20176
11
Proceedings of the 1st international workshop on Multimodal crowd sensing
20121
12 201222
13 201211
14 20116
15 20105
16 201027
17 201017
18 20108
19 20093
20 200810

About Haggai Roitman

Haggai Roitman is a scholar working on Information Systems, Artificial Intelligence and Management Science and Operations Research, having authored 75 papers that have together received 869 indexed citations. Recurring topics across this work include Topic Modeling (20 papers), Information Retrieval and Search Behavior (17 papers), Web Data Mining and Analysis (16 papers), Data Quality and Management (13 papers), Data Management and Algorithms (11 papers), Semantic Web and Ontologies (11 papers), Recommender Systems and Techniques (10 papers) and Natural Language Processing Techniques (10 papers). The work is most often cited by research in Information Systems (402 citations), Artificial Intelligence (478 citations) and Signal Processing (130 citations). Haggai Roitman has collaborated with scholars based in Israel, United States and Spain. Frequent co-authors include David Carmel, Avigdor Gal, Naama Zwerdling, Yosi Mass, David Carmel, Guy Feigenblat, David Konopnicki, Elad Yom‐Tov, Roee Shraga and Michal Shmueli-Scheuer. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, IBM Journal of Research and Development and Proceedings of the VLDB Endowment.

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.

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