Countries citing papers authored by Lawrence B. Holder
Since
Specialization
Citations
This map shows the geographic impact of Lawrence B. Holder'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 Lawrence B. Holder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lawrence B. Holder more than expected).
Fields of papers citing papers by Lawrence B. Holder
This network shows the impact of papers produced by Lawrence B. Holder. 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 Lawrence B. Holder. The network helps show where Lawrence B. Holder may publish in the future.
Co-authorship network of co-authors of Lawrence B. Holder
This figure shows the co-authorship network connecting the top 25 collaborators of Lawrence B. Holder.
A scholar is included among the top collaborators of Lawrence B. Holder 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 Lawrence B. Holder. Lawrence B. Holder is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eberle, William & Lawrence B. Holder. (2009). Discovering Anomalies to Multiple Normative Patterns in Structural and Numeric Data. The Florida AI Research Society.1 indexed citations
4.
You, Chang, et al.. (2008). Dynamic Graph-based Relational Learning of Temporal Patterns in Biological Networks Changing over Time.. 984–990.1 indexed citations
5.
You, Chang, et al.. (2007). Learning Node Replacement Graph Grammars in Metabolic Pathways.. 44–50.11 indexed citations
6.
Youngblood, G. Michael, et al.. (2006). Building Test Beds for AI with the Q3 Mode Base. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 2(1). 153–154.6 indexed citations
Youngblood, G. Michael, et al.. (2005). Automation intelligence for the smart environment. International Joint Conference on Artificial Intelligence. 1513–1514.23 indexed citations
9.
Youngblood, G. Michael, Diane J. Cook, & Lawrence B. Holder. (2005). A learning architecture for automating the intelligent environment. Innovative Applications of Artificial Intelligence. 1576–1581.9 indexed citations
10.
Cook, Diane J., et al.. (2005). A Serial Partitioning Approach to Scaling Graph-Based Knowledge Discovery.. The Florida AI Research Society. 188–193.1 indexed citations
11.
Holder, Lawrence B., et al.. (2004). Attribute-Value Selection Based on Minimum Description Length.. International Conference on Artificial Intelligence. 1154–1159.3 indexed citations
12.
Holder, Lawrence B., et al.. (2003). MDL-Based Context-Free Graph Grammar Induction. The Florida AI Research Society. 351–355.8 indexed citations
13.
Cook, Diane J., et al.. (2003). Identifying Inhabitants of an Intelligent Environment Using a Graph-Based Data Mining System.. The Florida AI Research Society. 314–318.4 indexed citations
14.
Bandyopadhyay, Sanghamitra, et al.. (2002). Enhancing Structure Discovery for Data Mining in Graphical Databases Using Evolutionary Programming. The Florida AI Research Society. 232–236.8 indexed citations
15.
Cook, Diane J. & Lawrence B. Holder. (2001). A Client-Server Interactive Tool for Integrated Artificial Intelligence Curriculum. The Florida AI Research Society. 206–210.3 indexed citations
16.
Cook, Diane J., et al.. (2001). Structural Web Search Using a Graph-Based Discovery System. The Florida AI Research Society. 133–137.4 indexed citations
17.
Holder, Lawrence B., et al.. (2000). Graph-Based Hierarchical Conceptual Clustering in Structural Databases. National Conference on Artificial Intelligence. 1078.4 indexed citations
18.
Cook, Diane J., et al.. (2000). Discovering Structural Patterns in Telecommunications Data. The Florida AI Research Society. 82–85.6 indexed citations
19.
Holder, Lawrence B., et al.. (1999). Applying the Subdue Substructure Discovery System to the Chemical Toxicity Domain. The Florida AI Research Society. 90–94.17 indexed citations
20.
Holder, Lawrence B.. (1992). Empirical analysis of the general utility problem in machine learning. National Conference on Artificial Intelligence. 249–254.5 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.