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.
On Clustering Validation Techniques
20011.8k citationsMichalis Vazirgiannis et al.profile →
Countries citing papers authored by Michalis Vazirgiannis
Since
Specialization
Citations
This map shows the geographic impact of Michalis Vazirgiannis'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 Michalis Vazirgiannis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michalis Vazirgiannis more than expected).
Fields of papers citing papers by Michalis Vazirgiannis
This network shows the impact of papers produced by Michalis Vazirgiannis. 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 Michalis Vazirgiannis. The network helps show where Michalis Vazirgiannis may publish in the future.
Co-authorship network of co-authors of Michalis Vazirgiannis
This figure shows the co-authorship network connecting the top 25 collaborators of Michalis Vazirgiannis.
A scholar is included among the top collaborators of Michalis Vazirgiannis 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 Michalis Vazirgiannis. Michalis Vazirgiannis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nikolentzos, Giannis, et al.. (2020). Rep the Set: Neural Networks for Learning Set Representations. International Conference on Artificial Intelligence and Statistics. 1410–1420.8 indexed citations
Malliaros, Fragkiskos D. & Michalis Vazirgiannis. (2017). Graph-based Text Representations: Boosting Text Mining, NLP and Information Retrieval with Graphs. Empirical Methods in Natural Language Processing.3 indexed citations
11.
Tixier, Antoine J.‐P., Giannis Nikolentzos, Polykarpos Meladianos, & Michalis Vazirgiannis. (2017). Classifying Graphs as Images with Convolutional Neural Networks.. arXiv (Cornell University).5 indexed citations
12.
Nikolentzos, Giannis, et al.. (2015). AUEB at TREC 2015: Clinical Decision Support Track.. Text REtrieval Conference.2 indexed citations
Gunopulos, Dimitrios, Thomas Hofmann, Donato Malerba, & Michalis Vazirgiannis. (2011). Machine Learning and Knowledge Discovery in Databases, Part III: European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, ... / Lecture Notes in Artificial Intelligence). Springer eBooks.4 indexed citations
15.
Eirinaki, Magdalini, et al.. (2008). Introducing Semantics in Web Personalization: The Role of Ontologies.2 indexed citations
16.
Vazirgiannis, Michalis, et al.. (2008). Using tri-training and support vector machines for addressing the ecml-pkdd 2006 discovery challenge.3 indexed citations
17.
Doulkeridis, Christos, Akrivi Vlachou, Yannis Kotidis, & Michalis Vazirgiannis. (2007). Peer-to-peer similarity search in metric spaces. Very Large Data Bases. 986–997.37 indexed citations
18.
Stefanakis, Emmanuel, Michalis Vazirgiannis, & Timos Sellis. (1996). Incorporating Fuzzy Logic Methodologies into GIS Operations. DSpace - NTUA (National Technical University of Athens).5 indexed citations
19.
Vazirgiannis, Michalis, Alexandros Kalousis, & M. Hatzopoulos. (1995). KNOWEL: A Hypermedia Knowledge Editor.. 178–187.
20.
Vazirgiannis, Michalis, et al.. (1993). Hypermedia and Knowledge Representation: An Object-Oriented Design Based on Fuzzy Logic.. Software Engineering and Knowledge Engineering. 337–342.2 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.