Citations per year, relative to Eugene C. Freuder Eugene C. Freuder (= 1×)
peers
Ian P. Gent
Countries citing papers authored by Eugene C. Freuder
Since
Specialization
Citations
This map shows the geographic impact of Eugene C. Freuder'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 Eugene C. Freuder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene C. Freuder more than expected).
Fields of papers citing papers by Eugene C. Freuder
This network shows the impact of papers produced by Eugene C. Freuder. 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 Eugene C. Freuder. The network helps show where Eugene C. Freuder may publish in the future.
Co-authorship network of co-authors of Eugene C. Freuder
This figure shows the co-authorship network connecting the top 25 collaborators of Eugene C. Freuder.
A scholar is included among the top collaborators of Eugene C. Freuder 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 Eugene C. Freuder. Eugene C. Freuder is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Swearngin, Amanda, Berthe Y. Choueiry, & Eugene C. Freuder. (2011). A Reformulation Strategy for Multi-Dimensional CSPs: The Case Study of the SET Game. Insecta mundi.1 indexed citations
2.
Freuder, Eugene C.. (2006). Constraints: the ties that bind. National Conference on Artificial Intelligence. 33(4). 1520–1523.4 indexed citations
3.
O’Sullivan, Barry, et al.. (2005). Corrective explanation for interactive constraint satisfaction. International Joint Conference on Artificial Intelligence. 1531–1532.3 indexed citations
4.
Zhang, Yuanlin & Eugene C. Freuder. (2004). Tractable tree convex constraint networks. National Conference on Artificial Intelligence. 197–202.5 indexed citations
5.
Freuder, Eugene C., et al.. (2003). Computing Explanations and Implications in Preference-based Configurators. Research Padua Archive (University of Padua).2 indexed citations
Sabin, Mihaela, et al.. (1999). A constraint-based approach to fault management for groupware services, Integrated Network Management, 1999. University of New Hampshire Scholars Repository (University of New Hampshire at Manchester).4 indexed citations
8.
Sabin, Mihaela & Eugene C. Freuder. (1999). Detecting and resolving inconsistency and redundancy in conditional constraint satisfaction problems. University of New Hampshire Scholars Repository (University of New Hampshire at Manchester).20 indexed citations
9.
Freuder, Eugene C. & D.D. Sabin. (1997). Interchangeability supports abstraction and reformulation for multi-dimensional constraint satisfaction. National Conference on Artificial Intelligence. 191–196.15 indexed citations
10.
Freuder, Eugene C., et al.. (1996). Neighborhood inverse consistency preprocessing. National Conference on Artificial Intelligence. 202–208.34 indexed citations
11.
Sabin, Mihaela & Eugene C. Freuder. (1996). Automated formulation of constraint satisfaction problems. University of New Hampshire Scholars Repository (University of New Hampshire at Manchester). 1407–1407.1 indexed citations
12.
Sqalli, Mohammed H. & Eugene C. Freuder. (1996). Inference-based constraint satisfaction supports explanation. National Conference on Artificial Intelligence. 318–325.20 indexed citations
13.
Freuder, Eugene C., et al.. (1996). Agent cooperation can compensate for agent ignorance in constraint satisfaction. National Conference on Artificial Intelligence. 24–29.5 indexed citations
14.
Freuder, Eugene C., et al.. (1995). Extracting constraint satisfaction subproblems. International Joint Conference on Artificial Intelligence. 548–555.17 indexed citations
15.
Freuder, Eugene C. & Richard J. Wallace. (1995). Generalizing inconsistency learning for constraint satisfaction. International Joint Conference on Artificial Intelligence. 563–569.2 indexed citations
16.
Sabin, D.D., Mihaela Sabin, Robert D. Russell, & Eugene C. Freuder. (1995). A constraint-based approach to diagnosing configuration problems. University of New Hampshire Scholars Repository (University of New Hampshire at Manchester). 121(1). 53–8.2 indexed citations
17.
Freuder, Eugene C., et al.. (1993). Using inferred disjunctive constraints to decompose constraint satisfaction problems. International Joint Conference on Artificial Intelligence. 254–260.6 indexed citations
18.
Freuder, Eugene C., et al.. (1992). An efficient cross product representation of the constraint satisfaction problem search space. National Conference on Artificial Intelligence. 421–427.20 indexed citations
19.
Freuder, Eugene C.. (1990). Complexity of K-tree structured constraint satisfaction problems. National Conference on Artificial Intelligence. 4–9.82 indexed citations
20.
Freuder, Eugene C. & Michael J. Quinn. (1985). Taking advantage of stable sets of variables in constraint satisfaction problems. International Joint Conference on Artificial Intelligence. 1076–1078.68 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.