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.
Countries citing papers authored by Adriano Veloso
Since
Specialization
Citations
This map shows the geographic impact of Adriano Veloso'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 Adriano Veloso with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adriano Veloso more than expected).
This network shows the impact of papers produced by Adriano Veloso. 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 Adriano Veloso. The network helps show where Adriano Veloso may publish in the future.
Co-authorship network of co-authors of Adriano Veloso
This figure shows the co-authorship network connecting the top 25 collaborators of Adriano Veloso.
A scholar is included among the top collaborators of Adriano Veloso 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 Adriano Veloso. Adriano Veloso is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pimentel, Tiago, et al.. (2018). A Generalized Active Learning Approach for Unsupervised Anomaly Detection. arXiv (Cornell University).9 indexed citations
9.
Pimentel, Tiago, Adriano Veloso, & Nívio Ziviani. (2017). Unsupervised and Scalable Algorithm for Learning Node Representations. International Conference on Learning Representations.10 indexed citations
Laender, Alberto H. F., et al.. (2013). FS-NER. 597–604.20 indexed citations
12.
Barbosa, G. A., et al.. (2013). Caracterização do uso de hashtags do Twitter para mensurar o sentimento da população online: Um estudo de caso nas Eleições Presidenciais dos EUA em 2012..1 indexed citations
13.
Veloso, Adriano, et al.. (2012). Named Entity Disambiguation in Streaming Data. Meeting of the Association for Computational Linguistics. 815–824.26 indexed citations
14.
Laender, Alberto H. F., et al.. (2012). Alleviating the Sparsity Problem in Recommender Systems by Exploring Underlying User Communities.. 35–47.3 indexed citations
15.
Veloso, Adriano, Marcos André Gonçalves, & Wagner Meira. (2011). Competence-Conscious Associative Rank Aggregation. Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais). 2(3). 337–352.4 indexed citations
16.
Guerra, Pedro, et al.. (2011). Exploiting Temporal Locality to Determine User Bias in Microblogging Platforms. Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais). 2(3). 273–288.
Veloso, Adriano & Wagner Meira. (2005). Rule Generation and Rule Selection Techniques for Cost-Sensitive Associative Classification.. 295–309.4 indexed citations
19.
Veloso, Adriano, et al.. (2003). Efficient, Accurate and Privacy-Preserving Data Mining for Frequent Itemsets in Distributed Databases.. 281–292.10 indexed citations
20.
Veloso, Adriano, et al.. (2002). Mining Reliable Models of Associations in Dynamic Databases.. 263–277.1 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.