James Cussens
About
In The Last Decade
James Cussens
61 papers receiving 657 citations
Peers
Comparison fields: 5 of 117
- Artificial Intelligence 505
- Management Science and Operations Research 129
- Information Systems 77
- Computational Theory and Mathematics 75
- Molecular Biology 56
Countries citing papers authored by James Cussens
This map shows the geographic impact of James Cussens'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 James Cussens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Cussens more than expected).
Fields of papers citing papers by James Cussens
This network shows the impact of papers produced by James Cussens. 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 James Cussens. The network helps show where James Cussens may publish in the future.
Co-authorship network of co-authors of James Cussens
This figure shows the co-authorship network connecting the top 25 collaborators of James Cussens. A scholar is included among the top collaborators of James Cussens 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 James Cussens. James Cussens is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | Dual Formulation of the Chordal Graph Conjecture | 1 |
| 6 | Kernel-based Approach for Learning Causal Graphs from Mixed Data. | 1 |
| 7 | GOBNILP: Learning Bayesian network structure with integer programming. | 1 |
| 8 | 70 | |
| 9 | 3 | |
| 10 | 86 | |
| 11 | 14 | |
| 12 | 1 | |
| 13 | Advances in Bayesian network learning using integer programming | 46 |
| 14 | Discriminative clustering for content-based tag recommendation in social bookmarking systems | 2 |
| 15 | Exploiting informative priors for Bayesian classification and regression trees | 7 |
| 16 | 5 | |
| 17 | 13 | |
| 18 | Statistical Aspects of Stochastic Logic Programs | 2 |
| 19 | Incorporating linguistic structure into statistical language models - Discussion | 1 |
| 20 | Experiments in Inductive Chart Parsing | 0 |
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.