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.
The global k-means clustering algorithm
20022.4k citationsAristidis Likas et al.Pattern Recognitionprofile →
The variational approximation for Bayesian inference
2008731 citationsDimitris Tzikas, Aristidis Likas et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Aristidis Likas
Since
Specialization
Citations
This map shows the geographic impact of Aristidis Likas'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 Aristidis Likas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aristidis Likas more than expected).
This network shows the impact of papers produced by Aristidis Likas. 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 Aristidis Likas. The network helps show where Aristidis Likas may publish in the future.
Co-authorship network of co-authors of Aristidis Likas
This figure shows the co-authorship network connecting the top 25 collaborators of Aristidis Likas.
A scholar is included among the top collaborators of Aristidis Likas 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 Aristidis Likas. Aristidis Likas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Likas, Aristidis, et al.. (2012). Dip-means: an incremental clustering method for estimating the number of clusters. Neural Information Processing Systems. 25. 2393–2401.47 indexed citations
Tzikas, Dimitris, Aristidis Likas, & N.P. Galatsanos. (2006). Variational Bayesian blind image deconvolution based on a sparse kernel model for the point spread function. European Signal Processing Conference. 1–5.
9.
Tsoulos, Ioannis G., I.E. Lagaris, & Aristidis Likas. (2005). Neural splines: exploiting parallelism for function approximation using modular neural networks. Neural, Parallel & Scientific Computations archive. 13(2). 161–178.
Stavroulakis, Georgios Ε., et al.. (2004). Neural network assisted crack and flow identification in transient dynamics. Journal of Theoretical and Applied Mechanics/Mechanika Teoretyczna i Stosowana. 42(3). 629–649.5 indexed citations
12.
Tzikas, Dimitris, Aristidis Likas, N.P. Galatsanos, Ana Lukić, & Miles N. Wernick. (2004). Bayesian regression of functional neuroimages. European Signal Processing Conference. 801–804.2 indexed citations
Likas, Aristidis, George Papageorgiou, & Andreas Stafylopatis. (1995). A parallelizable operation scheme of the Boltzmann machine optimizer based on group updates. DSpace - NTUA (National Technical University of Athens). 3(4). 451–465.1 indexed citations
19.
Likas, Aristidis, et al.. (1993). Embedding knowledge into stochastic learning automata for fast solution of binary constraint satisfaction problems.. The European Symposium on Artificial Neural Networks.2 indexed citations
20.
Likas, Aristidis, et al.. (1992). Collision-free movement of an autonomous vehicle using reinforcement learning. DSpace - NTUA (National Technical University of Athens). 666–670.7 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.