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.
Inductive Logic Programming: Theory and methods
1994713 citationsStephen Muggleton et al.profile →
Countries citing papers authored by Stephen Muggleton
Since
Specialization
Citations
This map shows the geographic impact of Stephen Muggleton'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 Stephen Muggleton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Muggleton more than expected).
Fields of papers citing papers by Stephen Muggleton
This network shows the impact of papers produced by Stephen Muggleton. 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 Stephen Muggleton. The network helps show where Stephen Muggleton may publish in the future.
Co-authorship network of co-authors of Stephen Muggleton
This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Muggleton.
A scholar is included among the top collaborators of Stephen Muggleton 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 Stephen Muggleton. Stephen Muggleton is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bundy, Alan, Nick Chater, & Stephen Muggleton. (2023). Introduction to ‘Cognitive artificial intelligence’. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 381(2251). 20220051–20220051.3 indexed citations
Huang, Yuxuan, et al.. (2021). Fast Abductive Learning by Similarity-based Consistency Optimization. Neural Information Processing Systems. 34.11 indexed citations
5.
Cropper, Andrew & Stephen Muggleton. (2015). Learning efficient logical robot strategies involving composable objects. Oxford University Research Archive (ORA) (University of Oxford). 3423–3429.9 indexed citations
6.
Lin, Dianhuan, et al.. (2014). Bias reformulation for one-shot function induction. DSpace@MIT (Massachusetts Institute of Technology).12 indexed citations
Muggleton, Stephen, Alireza Tamaddoni‐Nezhad, & Francesca A. Lisi. (2011). Proceedings of the 21st international conference on Inductive Logic Programming.2 indexed citations
9.
Lodhi, Huma & Stephen Muggleton. (2010). Elements of computational systems biology. CERN Document Server (European Organization for Nuclear Research).22 indexed citations
10.
Chen, Jianzhong, Stephen Muggleton, & José Sántos. (2007). Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning..2 indexed citations
11.
Muggleton, Stephen. (2006). Towards chemical universal turing machines. National Conference on Artificial Intelligence. 1527–1529.1 indexed citations
12.
Tamaddoni‐Nezhad, Alireza & Stephen Muggleton. (2001). Using Genetic Algorithms for learning clauses in first-order logic. Genetic and Evolutionary Computation Conference. 639–646.2 indexed citations
13.
Reiser, Philip G. K., Ross D. King, Douglas B. Kell, et al.. (2001). Developing a logical model of yeast metabolism. University of Salford Institutional Repository (University of Salford). 5. 223–244.14 indexed citations
14.
Muggleton, Stephen. (2000). Learning Stochastic Logic Programs. KTH Publication Database DiVA (KTH Royal Institute of Technology). 4. 141–153.42 indexed citations
15.
Furukawa, K., Donald Michie, & Stephen Muggleton. (1994). Machine intelligence 13: machine intelligence and inductive learning. Oxford University Press eBooks.7 indexed citations
16.
Michie, Donald, et al.. (1994). Machine intelligence and inductive learning. Oxford University Press eBooks.6 indexed citations
17.
Muggleton, Stephen. (1992). Developments in Inductive Logic Programming, Panel Position Paper.. Future Generation Computer Systems. 1071–1073.1 indexed citations
18.
Bain, Michael & Stephen Muggleton. (1991). Non-monotonic Learning. 105–119.28 indexed citations
Muggleton, Stephen. (1987). Duce, an oracle-based approach to constructive induction. International Joint Conference on Artificial Intelligence. 287–292.61 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.