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.
Objective Criteria for the Evaluation of Clustering Methods
This map shows the geographic impact of William Rand'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 William Rand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Rand more than expected).
This network shows the impact of papers produced by William Rand. 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 William Rand. The network helps show where William Rand may publish in the future.
Co-authorship network of co-authors of William Rand
This figure shows the co-authorship network connecting the top 25 collaborators of William Rand.
A scholar is included among the top collaborators of William Rand 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 William Rand. William Rand is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Ming‐Hui, William Rand, & Roland T. Rust. (2016). Don’t Do It Right, Do It Fast?Speed and Quality of Innovation as an Emergent Process. International Conference on Information Systems.1 indexed citations
6.
Stonedahl, Forrest & William Rand. (2008). Multi-Agent Learning with a Distributed Genetic Algorithm Exploring Innovation Diffusion on Networks. SSRN Electronic Journal.2 indexed citations
7.
Stonedahl, Forrest, William Rand, & Uri Wilensky. (2008). Multi-Agent Learning with a Distributed Genetic Algorithm. SSRN Electronic Journal.2 indexed citations
8.
Rand, William, John H. Holland, & Rick Riolo. (2005). Controlled observations of the genetic algorithm in a changing environment: Case studies using the shaky ladder hyperplane -defined functions.. Deep Blue (University of Michigan).5 indexed citations
Punch, William F. & William Rand. (2000). GP+Echo+subsumption = improved problem solving. Genetic and Evolutionary Computation Conference. 411–418.1 indexed citations
Dodman, Nicholas H., et al.. (1994). Equine self-mutilation syndrome (57 cases). Journal of the American Veterinary Medical Association. 204(8). 1219–1223.22 indexed citations
Rand, William. (1987). Food composition data : a user's perspective : report of a conference held in Logan, Utah, USA, 26-29 March 1985.2 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.