Magdalini Eirinaki

2.8k total citations · 1 hit paper
70 papers, 1.7k citations indexed

About

Magdalini Eirinaki is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Magdalini Eirinaki has authored 70 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Information Systems, 26 papers in Artificial Intelligence and 20 papers in Computer Networks and Communications. Recurrent topics in Magdalini Eirinaki's work include Recommender Systems and Techniques (22 papers), Web Data Mining and Analysis (14 papers) and Complex Network Analysis Techniques (13 papers). Magdalini Eirinaki is often cited by papers focused on Recommender Systems and Techniques (22 papers), Web Data Mining and Analysis (14 papers) and Complex Network Analysis Techniques (13 papers). Magdalini Eirinaki collaborates with scholars based in United States, Greece and Portugal. Magdalini Eirinaki's co-authors include Michalis Vazirgiannis, Iraklis Varlamis, Japinder Singh, Malamati Louta, Jerry Gao, Konstantinos Tserpes, Neoklis Polyzotis, Lise Getoor, Pigi Kouki and Shobeir Fakhraei and has published in prestigious journals such as IEEE Access, IEEE Transactions on Knowledge and Data Engineering and Future Generation Computer Systems.

In The Last Decade

Magdalini Eirinaki

65 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Web mining for web personalization

2003 475 citations Magdalini Eirinaki, Michalis Vazirgiannis profile →

Peers

Magdalini Eirinaki

IS

AI

CNC

SP

SPS

Flora Amato Italy

Ronny Lempel Israel

Giuseppe Fenza Italy

Domenico Ursino Italy

Domenico Rosaci Italy

Jari Veijalainen Finland

Gillian Dobbie New Zealand

Dianshuang Wu Australia

Christos Makris Greece

Liliana Ardissono Italy

Flora Amato Italy

Magdalini Eirinaki

548 ×0.5

IS

507 ×0.8

AI

369 ×1.0

CNC

165 ×0.7

SP

135 ×0.6

SPS

Citations per year, relative to Magdalini Eirinaki Magdalini Eirinaki (= 1×) peers Flora Amato

Countries citing papers authored by Magdalini Eirinaki

Since Specialization

Citations

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

Fields of papers citing papers by Magdalini Eirinaki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Magdalini Eirinaki

This figure shows the co-authorship network connecting the top 25 collaborators of Magdalini Eirinaki. A scholar is included among the top collaborators of Magdalini Eirinaki 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 Magdalini Eirinaki. Magdalini Eirinaki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Potika, Katerina, et al.. (2025). Special issue on big data computing service and machine learning applications. Future Generation Computer Systems. 171. 107836–107836.

2.

Eirinaki, Magdalini, et al.. (2024). Scalable and Autonomous Network Defense Using Reinforcement Learning. IEEE Access. 12. 92919–92930. 2 indexed citations

3.

Wang, John, et al.. (2023). Using Graph Neural Networks for Social Recommendations. Algorithms. 16(11). 515–515.

4.

Eirinaki, Magdalini, et al.. (2022). Real-time recommendations for energy-efficient appliance usage in households. Frontiers in Big Data. 5. 972206–972206. 10 indexed citations

5.

Eirinaki, Magdalini, et al.. (2019). A Graph-Based Recommender System for Food Products. 83–87. 6 indexed citations

6.

Eirinaki, Magdalini, et al.. (2019). ViSeR: A Visual Search Engine for e-Retail. 76–83. 5 indexed citations

7.

Potika, Katerina, et al.. (2019). Exploratory data analysis and crime prediction for smart cities. 1–9. 10 indexed citations

8.

Eirinaki, Magdalini, Nuno Moniz, & Katerina Potika. (2016). Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 408–415. 7 indexed citations

9.

Moniz, Nuno, Luı́s Torgo, & Magdalini Eirinaki. (2016). Time-Based Ensembles for Prediction of Rare Events in News Stream. San José State University ScholarWorks (San Jose State University). abs 1412 436. 1066–1073. 3 indexed citations

10.

Eirinaki, Magdalini, et al.. (2016). PRO-Fit: A personalized fitness assistant framework. Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering. 2016. 386–389. 12 indexed citations

11.

Eirinaki, Magdalini, et al.. (2015). QueRIE reloaded: Using matrix factorization to improve database query recommendations. 8 indexed citations

12.

Eirinaki, Magdalini, et al.. (2014). Using Social Data for Personalizing Review Rankings. Conference on Recommender Systems. 6 indexed citations

13.

Varlamis, Iraklis, et al.. (2014). Classification of movement data concerning user's activity recognition via mobile phones. 1–6. 10 indexed citations

14.

Eirinaki, Magdalini, et al.. (2011). The QueRIE system for Personalized Query Recommendations.. IEEE Data(base) Engineering Bulletin. 34(2). 55–60. 20 indexed citations

15.

Eirinaki, Magdalini, et al.. (2011). Feature-based opinion mining and ranking. Journal of Computer and System Sciences. 78(4). 1175–1184. 165 indexed citations

16.

Eirinaki, Magdalini, et al.. (2010). SQL QueRIE Recommendations: a query fragment-based approach. San José State University ScholarWorks (San Jose State University). 3(2). 5 indexed citations

17.

Eirinaki, Magdalini, et al.. (2008). Introducing Semantics in Web Personalization: The Role of Ontologies. 2 indexed citations

18.

Eirinaki, Magdalini. (2008). Data Mining for Business Intelligence. 18 indexed citations

19.

Dalamagas, Theodore, et al.. (2007). Mining user navigation patterns for personalizing topic directories. RMIT Research Repository (RMIT University Library). 81–88. 6 indexed citations

20.

Eirinaki, Magdalini, et al.. (2005). Web path recommendations based on page ranking and Markov models. 2–9. 58 indexed 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.

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