Róbert Pálovics

5.3k citations
23 papers · 288 · h-index 10

Impact in

Papers in

Róbert Pálovics

22 papers receiving 282 citations

Peers

Róbert Pálovics
Comparison fields: 5 of 75
  • Neurology 56
  • Statistical and Nonlinear Physics 59
  • Information Systems 92
  • Developmental Neuroscience 15
  • Aging 6
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Citations per year

Countries citing papers authored by Róbert Pálovics

Since Specialization
Citations

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

Fields of papers citing papers by Róbert Pálovics

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Róbert Pálovics, 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 Róbert Pálovics Line = papers co-authored together Róbert Pálovics links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202388
2 201440
3 201623
4 201919
5 201818
6 201515
7 201614
8 202013
9 201311
10 202410
11 20246
12 20136
13 20215
14
Location-aware online learning for top-k hashtag recommendation
20154
15
Online ranking prediction in non-stationary environments
20173
16
Alpenglow: Open source recommender framework with time-Aware learning and evaluation
20173
17
Predicting User-specific Temporal Retweet Count Based on Network and Content Information.
20153
18 20153
19 20231
20 20141

About Róbert Pálovics

Róbert Pálovics is a scholar working on Information Systems, Statistical and Nonlinear Physics, Artificial Intelligence, Computer Science Applications and Computer Networks and Communications, having authored 23 papers that have together received 288 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (10 papers), Complex Network Analysis Techniques (7 papers), Human Mobility and Location-Based Analysis (3 papers), Single-cell and spatial transcriptomics (2 papers), Spam and Phishing Detection (2 papers), Online Learning and Analytics (2 papers), Peer-to-Peer Network Technologies (2 papers) and Advanced Bandit Algorithms Research (2 papers). The work is most often cited by research in Neurology (56 citations), Statistical and Nonlinear Physics (59 citations), Information Systems (92 citations), Developmental Neuroscience (15 citations) and Aging (6 citations). Róbert Pálovics has collaborated with scholars based in Hungary, United States and Germany. Frequent co-authors include András A. Benczúr, Levente Kocsis, Tamás Kiss, Bálint Daróczy, Patricia Moran‐Losada, Linda Partridge, Oliver Hãhn, Achint Kaur, Jacob M. Hull and Sebastian Grönke. Their work appears in journals such as Applied Network Science, Scientific Reports, Cells, EPJ Data Science and Communications Biology.

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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