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.
Resource description framework
2001619 citationsK. Selçuk Candan, Huan Liu 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 K. Selçuk Candan
Since
Specialization
Citations
This map shows the geographic impact of K. Selçuk Candan'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 K. Selçuk Candan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. Selçuk Candan more than expected).
Fields of papers citing papers by K. Selçuk Candan
This network shows the impact of papers produced by K. Selçuk Candan. 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 K. Selçuk Candan. The network helps show where K. Selçuk Candan may publish in the future.
Co-authorship network of co-authors of K. Selçuk Candan
This figure shows the co-authorship network connecting the top 25 collaborators of K. Selçuk Candan.
A scholar is included among the top collaborators of K. Selçuk Candan 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 K. Selçuk Candan. K. Selçuk Candan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Sicong, et al.. (2015). NOTES2: Networks-of-Traces for Epidemic Spread Simulations. ScholarWorks - Georgia State University (Georgia State University). 79–83.7 indexed citations
6.
Candan, K. Selçuk. (2014). Scalable retrieval and analysis of simulation and observation data sets. 5–6.1 indexed citations
Candan, K. Selçuk & Maria Luisa Sapino. (2010). Data Management for Multimedia Retrieval. Institutional Research Information System University of Turin (University of Turin).20 indexed citations
9.
Cataldi, Mario, Claudio Schifanella, K. Selçuk Candan, Maria Luisa Sapino, & Luigi Di. (2010). Context-informed Knowledge Extraction from Document Collections to Support User Navigation. 1. 74–94.
10.
Li, Qing, K. Selçuk Candan, & Qi Yan. (2008). Extracting relevant snippets for web navigation. National Conference on Artificial Intelligence. 1195–1200.5 indexed citations
Yan, Qi, K. Selçuk Candan, Maria Luisa Sapino, & Keith Kintigh. (2006). QUEST: Query-driven Exploration of Semistructured Data with ConflicTs and Partial Knowledge. Very Large Data Bases. 9–16.3 indexed citations
Candan, K. Selçuk, et al.. (2006). Keyword Weight Propagation for Indexing Structured Web Content.1 indexed citations
15.
Sapino, Maria Luisa, et al.. (2006). Log-Analysis based Characterization of Multimedia Documents for Effective Delivery of Distributed Multimedia Presentations. 73–76.2 indexed citations
Candan, K. Selçuk, et al.. (2004). Secure and privacy preserving outsourcing of tree structured data. Lecture notes in computer science. 3178. 1–17.1 indexed citations
18.
Candan, K. Selçuk, et al.. (2003). Fairly redistributing failed server load in a distributed system. Lecture notes in computer science. 2889. 871–884.1 indexed citations
19.
Candan, K. Selçuk. (1998). A framework for distributed multimedia collaborations.2 indexed citations
20.
Li, Wen‐Syan, K. Selçuk Candan, Kyoji Hirata, & Yoshinori Hara. (1997). Facilitating Multimedia Database Exploration through Visual Interfaces and Perpetual Query Reformulations. Very Large Data Bases. 538–547.10 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.